- Do you want to learn more about the environment, recycling and sustainability?
- Do you want to find solutions and design products and procedures that improve society?
- Would you like to learn more about energy and how it can be generated and used in a sustainable way?
- Do you want to make the world a better place?
MS students in mechanical engineering complete an extensive research project, often in partnership with a company, which can lead to job opportunities after graduation. Dynamic research, strong links with industry, and international partnerships ensure that student projects are based on real-world conditions and the most up-to-date knowledge.
Programme structure
The programme is 120 ECTS and is organised as two years of full-time study.
The programme is made up of:
- Courses, 60-90 ECTS
- Research project, 30-60 ECTS
Specialisations
Students may choose between the following specialisation
- Mechanical Engineering.
- Renewable Energy - Geothermal Engineering. See more programmes with a specialisation in renewable energy.
Most or all courses are electives, chosen in consultation with the administrative supervisor to suit the student's areas of interest.
Organisation of teaching
This programme is taught in Icelandic but the learning material is generally in English.
There are various funding opportunities for Master's students and good work facilities are available.
Main objectives
Students should acquire the knowledge and skills required to analyse and resolve complex problems in various specialist areas of mechanical engineering. Students will learn to adopt independent and creative thinking in their approach to problems and develop the ability to analyse issues to produce clear conclusions.
Students should become familiar with the most up-to-date knowledge in mechanical engineering.
Other
After completing the Master's degree in mechanical engineering, students can apply for the right to use the title of engineer. This professional title is legally protected.
Completing a Master's degree in mechanical engineering allows you to apply for doctoral studies.
- Applicants must have completed a BS degree in mechanical engineering or similar subjects at the University of Iceland, or a comparable degree from another University with a grade point average of at least 6.5 on a scale from 1-10. Additional Prerequisite courses may be required.
- All international applicants, whose native language is not English, are required to provide results of the TOEFL (79) or IELTS (6.5) tests as evidence of English proficiency.
- Applicants are asked to submit a letter of motivation, 1 page, where they should state the reasons they want to pursue graduate work, their academic goals and a suggestion or outline for a final paper.
- Letters of recommendation (2) should be submitted. These should be from faculty members or others who are familiar with your academic work and qualified to evaluate your potential for graduate study. Please ask your referees to send their letters of recommendation directly to the University of Iceland electronically by e-mail (PDF file as attachment) to transcript@hi.is.
120 ECTS credits have to be completed for the qualification. Organised as a two-year programme. The study is either 90 ECTS credits in courses and 30 ECTS credits in an individual project or 60 ECTS credits in courses and 60 ECTS credits in an individual project. The choice between 30 and 60 ECTS credit MS projects must be agreed by a faculty advisor.
- CV
- Statement of purpose
- Reference 1, Name and email
- Reference 2, Name and email
- Certified copies of diplomas and transcripts
- Proof of English proficiency
Further information on supporting documents can be found here
Programme structure
Check below to see how the programme is structured.
- First year
- Fall
- Year unspecified
- Fall
- Geothermal Power Plants
- Thesis
- Geothermal Wells
- Not taught this semesterFish Processing Technology 1
- Not taught this semesterQuality Management
- Not taught this semesterComputational Intelligence
- Operations Research 2
- Medical Imaging Systems
- Cloud Computing and Big Data
- Machine Learning
- Membrane Technology
- Final project
- Selected Topics in Mechanical Engineering
- Not taught this semesterEnergy Intensive Production Processes
- Computational Structural Mechanics
- Vibration Analysis
- Design Optimization
- Corrosion
- Condensed Matter Physics 1
- Time Series Analysis
- Continuum Mechanics and Heat Transfer
- Applied Linear Statistical Models
- Thesis skills: project management, writing skills and presentation
- Spring 1
- Food Engineering 2
- Not taught this semesterEngineering Design Processes
- Mechatronics
- Computer Aided Design
- Plastic, Metals and Fibre Composites
- Thesis
- Direct Geothermal Utilization
- Not taught this semesterFish Processing Technology 2
- Not taught this semesterIntroduction to Nanotechnology
- Not taught this semesterInnovation, Product Development, Marketing
- Field Course in Innovation and Entrepreneurship (II)
- Field Course in Innovation and Entrepreneurship (I)
- Not taught this semesterIntroduction to Systems Biology
- Robotics and Computer Vision
- Science and innovation in medical technology
- The AI lifecycle
- Final project
- Selected Topics in Mechanical Engineering
- Computational Fluid Dynamics
- High Performance Computing
- Selected Topics in Mechanical Engineering
- Year unspecified
- Fish Processing Technology 1
Geothermal Power Plants (VÉL114F)
The main topics of the course are:
- Geothermal power plants worldwide and in Iceland.
- Thermodynamics in flash power plants, power cycles.
- Steam separators, condensers, incondensable gases, cooling.
- Alternative power cycles, ORC and Kalina power plants.
- Co-production of heat and power.
- Mechanical design of pipe systems, especially for steam and steam gathering.
- Scaling and corrosion. Environmental effects related to geothermal utilization.
- Economics and cost, both in building phase and operation.
- Exergy and its relation to environmental conditions.
- Energy and exergy analysis, Sankey og Grassmann charts.
- Flow of energy cost, thermo-economics and estimation of net cost in improving parts of energy systems.
- Production control and equipment for process regulation.
Thesis (VÉL118F)
Students are given the opportunity to suggest an idea for project topics in consultation with faculty members.
Geothermal Wells (VÉL120F)
The main topics of the course are:
- Geothermal wells, different types and drilling methods.
- Well casings and mechanical design of geothermal wells.
- Well cementing, work procedures and standards.
- Wellheads and their design. Load on wellheads and related security issues.
- Well logging and measurements along wells. Methods for measuring temperature and pressure.
- Well flow initiation and measurement of the mass flow of steam and water. Energy flow from geothermal wells and potential power production capacity.
- Two phase flow of steam and water. Flow in wells and steam gathering pipes on the surface. Flow properties and determination of flow regimes. Pressure variations in two phase flow.
Fish Processing Technology 1 (VÉL502M)
The role of the fish industry in the Icelandic economy. Fish as raw material, its composition, physical and chemical properties. Fish stocks, fishing gear, selectivity. Storage methods on board and after landing. Processing methods, production process and processing equipment for cooling, superchilling, freezing, salting, drying, canning and shell process. Energy and mass balance for each step in the process and the whole process.
Quality Management (IÐN101M)
Organization and management systems. The systems approach. Quality management, quality concepts. Historical development of quality management. Quality cost. Quality in manufacturing. x, R, p, c and cusum-chart. Statistical quality control. Tests of hypotheses. Acceptance sampling - OC curves. Inspection planning. Quality systems and quality assurance. Quality handbook and organizing for quality. ISO 9001. Total Quality Management, improvement step by step, motivations theories. Quality tools. Practical assignment: Designing a quality system for a company.
Computational Intelligence (IÐN102M)
Basic aspects of computational intelligence, which is the study of algorithms that improve automatically through experience.
Operations Research 2 (IÐN508M)
This course will introduce the student to decision and optimization models in operations research. On completing the course the student will be able to formulate, analyze, and solve mathematical models, which represent real-world problems, and critically interpret their results. The course will cover integer programming and modeling with stochastic programming. The student will become familiar with building mathematical models using Python.
Medical Imaging Systems (RAF507M)
Introduction to the instrumentation, physics and signal processing methods used in medical imaging systems from a signal processing perspective. The modalities covered include projection radiography, X-ray computed tomography, nuclear medicine (i.e. SPECT and PET), ultrasound, and magnetic resonance imaging. The primary focus is on the methods required to reconstruct images within each modality, the kind of signals being measured and how these data culminate in an image. Attention will also be given to image quality in each modality, including resolution, contrast, signal-to-noise ratio, and distortion of images.
Cloud Computing and Big Data (REI504M)
Overview of high performance computing (HPC) and “Big Data”, HPC environments with computing, network and storage resources, overview of parallel programming. Storage infrastructures and services for Big Data, Big Data analytics, the map-reduce paradigm, structured and unstructured data. Practical exercises: (A) Students will use the Amazon Web Services (AWS) cloud or equivalent to set up a multi-computer web service and an associated multi-computer testing application. (B) Students will get hands on experience of processing large data sets using map-reduce techniques with AWS.
Machine Learning (REI505M)
An overview of some of the main concepts, techniques and algorithms in machine learning. Supervised learning and unsupervised learning. Data preprocessing and data visualization. Model evaluation and model selection. Linear regression, nearest neighbors, support vector machines, decision trees and ensemble methods. Deep learning. Cluster analysis and the k-means algorithm. The students implement simple algorithms in Python and learn how to use specialized software packages. At the end of the course the students work on a practical machine learning project.
Membrane Technology (UMV501M)
Objectives: This course is to provide an understanding of membrane technology applied in various industries, such as utilities (water and sewer), environmental industry, food industry, pharmaceutical industry, and chemical/biochemical industry.
Topics: (1) Membrane technology as a solution in industries (separation and purification of food, pharmaceutical, and chemical products) and in environments (water and wastewater treatment; air pollution control; nutrients recovery and reuse); (2) Membrane materials, chemical-based synthesis methods, modifications; (3) Membrane physical, chemical, and mechanical properties and characterization; (4) Transport phenomena in membrane processes; (5) Membrane fouling and fouling mitigation; (6) Membrane operation unit (such as microfiltration, ultrafiltration, nanofiltration, reverse osmosis, forward osmosis, pressure retarded osmosis, membrane distillation, electrodialysis, gas separation) and their applications in industries; (7) Hybrid membrane processes and their applications in industries; (8) Membrane system design.
Teaching: Lectures (teaching lecture, tutorial lecture, lab lecture) and a group project. Teaching lectures introduce the fundamentals and advances of membrane technology, the application of membrane technology in industry. Tutorial lectures are provided to discuss calculation questions and solutions with students. Lab lecture is performed in the research lab to demonstrate selected membrane processes and allow students hands-on practice. In the group project, students review literatures of a selected topic relating to advanced membrane technology, write a report, and give an oral presentation.
The course is also suitable for students specializing in other fields than Civil or Environmental Engineering, e.g., Chemical engineering, Industrial Engineering, Mechanical Engineering, Bioengineering, and Food science.
Final project (VÉL441L)
- The topic of the Master's thesis must be chosen under the guidance of the supervisor and the Faculty Coordinator of the student. The thesis represents 30 or 60 credits. All Master's student have been assigned to a Faculty Coordinator from the beginning of their studies, who advises the student regarding the organization of the program. If a student does not have a supervisor for the final project, he / she must turn to the Faculty Coordinator for assistance.
- The choice of topic is primarily the responsibility of the student in collaboration with his or her project supervisor. The topic of the project should fall within the student's area of study, i.e. course of study and chosen specialisation.
- The master’s student writes a thesis according to the School’s template and defends it in a master’s defense.
- Final project exam is divided into two parts: Oral examination and open lecture
- Present at the oral exam is the student, supervisor, examiner and members of the Master's committee. The student presents a brief introduction on his / her project. It is important that the objectives and research question(s) are clearly stated, and that main findings and lessons to be drawn from the project are discussed.
- The student delivers a thesis and a project poster.
- According to the rules of the Master's program, all students who intend to graduate from the School of Engineering and Natural Sciences need to give a public lecture on their final project.
- All students graduating from the University of Iceland shall submit an electronic copy of their final Master's thesis to Skemman.is. Skemman is the digital repository for all Icelandic universities and is maintained by the National and University Library.
- According to regulations of University of Iceland all MS thesis should have open access after they have been submitted to Skemman.
Learning Outcomes:
Upon completion of an MS thesis, the student should be able to:
- Formulate engineering design project / research questions
- Use an appropriate theoretical framework to shed light on his / her topic
- Analyze and solve engineering tasks in a specialized field.
- Perform a literature search and a thorough review of the literature.
- Demonstrate initiative and independent creative thinking.
- Use economic methodology to answer a specific research question
- Competently discuss the current knowledge within the field and contribute to it with own research
- Work with results, analyze uncertainties and limitations and interpret results.
- Assess the scope of a research project and plan the work accordingly
- Effectively display results and provide logical reasoning and relate results to the state of knowledge.
Selected Topics in Mechanical Engineering (VÉL049F)
Lectures on and study of selected topics in current research and recent development in the field of Mechanical engineering. Topics may vary.
Students contact the teacher and the chair of department regarding registration for the course.
Energy Intensive Production Processes (VÉL102M)
The aim of the course is:- To give students overview of processes in materials engineering;- To encourage students to think about feasible ways to utilize renewable energy. The course will cover the industrial processes in some of the larger Icelandic companies, including the production of ferro-alloys, aluminium smelting, rockwool production, recycling of steel, algea and diatomitemining, and production of sodium chlorine, fertilizers, cement. The course will also cover some of the larger material engineering processes that are not in practice in Iceland but may be a feasible option for Icelandic industry. Students will get good overview of the processes, required materials, source of power and power consumption, pollution, products etc. Discussions will be held on the financial background for individual processes, covering aspects such as production cost, profit and the influences of market share changes. Grades are based on 2 larger projects the students work on through the semester. Field trips are an important part of the course.
Computational Structural Mechanics (VÉL103M)
The aim of this course is to give students an exposure to the theoretical basis of the finite element method and its implementation principles. Furthermore, to introduce the use of available finite element application software for solving real-life engineering problems.
The course covers such topics as: stiffness matrices, elements stiffness matrix, system stiffness matrix, local and global stiffness, shape functions, isoparametric formulation and numerical integration. Various elements are studied, such as, trusses and beams, plane elements, 3D elements, plates and shells. Students mostly solve problems in solid mechanics (stress analysis) but can choose to work on a design project in other areas, such as vibrations or heat transfer.
The course includes class lectures and work sessions where students solve problems, both in python (can also choose matlab) and in the commercial software Ansys, under the supervison of the instructor. There is extensive use of Python (Matlab) and Ansys in solving homework problems and semester projects.
Vibration Analysis (VÉL101M)
Spectral and wavelet analysis. Linear and nonlinear systems. Envelope and cepstrum analysis Measurement, identification and response problems. Vibrations of continuous systems. Formulation of finite element model for analysis of dynamic problems. Fault diagnostic and machine condition monitoring.
Design Optimization (VÉL113F)
Optimum design concepts. Fundamentals of linear and nonlinear programming, constrained and unconstrained optimum design problems. Simulated annealing and genetic algorithms. Project and applications to realistic engineering design problems.
Corrosion (VÉL501M)
Basic thermodynamic and electrochemical principles that cause corrosion. Procedures of electrochemical measurements used to investigate corrosion behavior. Methods of corrosion protection and prevention, materials selection and design.
The course is taught every other year on even numbered years.
Condensed Matter Physics 1 (EÐL520M)
The course is an introduction to some basic concepts of condensed matter physics. Curriculum: Chemical bonds, crystal structure, crystal symmetry, the reciprocal lattice. Vibrational modes of crystals, phonons, specific heat, thermal conductivity. The free electron model, band structure of condensed matter, effective mass. Metals, insulators and semiconductors. The course includes three labs.
Time Series Analysis (IÐN113F)
ARMAX and other similar time series models. Non-stationary time series. Correlation and spectral analysis. Parameter estimation, parametric and non-parametric approaches, Least Squares and Maximum Likelihood. Model validation methods. Models with time dependent parameters. Numerical methods for minimization. Outlier detection and interpolation. Introduction to nonlinear time series models. Discrete state space models. Discrete state space models. Extensive use of MATLAB, especially the System Identification Toolbox.
Continuum Mechanics and Heat Transfer (JEÐ503M)
Objectives: To introduce continuum mechanics, fluid dynamics and heat transfer and their application to problems in physics and geophysics. I. Stress and strain, stress fields, stress tensor, bending of plates, models of material behaviour: elastic, viscous, plastic materials. II. Fluids, viscous fluids, laminar and turbulent flow, equation of continuity, Navier-Stokes equation. III. Heat transfer: Heat conduction, convection, advection and geothermal resources. Examples and problems from various branches of physics will be studied, particularly from geophysics.
Teaching statement: To do well in this course, students should actively participate in the discussions, attend lectures, give student presentations and deliver the problem sets assigned in the course. Students will gain knowledge through the lectures, but it is necessary to do the exercises to understand and train the use of the concepts. The exercises are intergrated in the text of the book, it is recommended to do them while reading the text. Instructors will strive to make the concepts and terminology accessible, but it is expected that students study independently and ask questions if something is unclear. In order to improve the course and its content, it is appreciated that students participate in the course evaluation, both the mid-term and the end of term course evaluation.
Applied Linear Statistical Models (STÆ312M)
The course focuses on simple and multiple linear regression as well as analysis of variance (ANOVA), analysis of covariance (ANCOVA) and binomial regression. The course is a natural continuation of a typical introductory course in statistics taught in various departments of the university.
We will discuss methods for estimating parameters in linear models, how to construct confidence intervals and test hypotheses for the parameters, which assumptions need to hold for applying the models and what to do when they are not met.
Students will work on projects using the statistical software R.
Thesis skills: project management, writing skills and presentation (VON001F)
Introduction to the scientific method. Ethics of science and within the university community.
The role of the student, advisors and external examiner. Effective and honest communications.
Conducting a literature review, using bibliographic databases and reference handling. Thesis structure, formulating research questions, writing and argumentation. How scientific writing differs from general purpose writing. Writing a MS study plan and proposal. Practical skills for presenting tables and figures, layout, fonts and colors. Presentation skills. Project management for a thesis, how to divide a large project into smaller tasks, setting a work plan and following a timeline. Life after graduate school and being employable.
Food Engineering 2 (MAT803F)
Course Description:
Objective: That students can evaluate food processes and calculate the main variables in different unit operations, plan and control food processes. To make students more capable of making decisions about changes in manufacture and transport processes.
In the lectures, the main food processes are reviewed:
- The effect of holding time and temperature in manufacturing processes and water content and water activity on the quality and properties of foods
- Processing/preservation methods such as chilling, superchilling, freezing and thawing, salting, smoking, heating and canning, drying, evaporation, separation and fermentation. Use of steam tables, enthalpy- and Mollier diagrams.
- Process flow diagrams/charts by process steps, material flow and balance calculations and risk analysis.
- Processing and packaging equipment and packaging for different foods
- Main parameters of production control.
- Storage conditions (light, humidity, temperature, air composition, etc.) and key factors affecting changes in food during storage, transportation and sale/distribution of food.
- Design considerations for food processing companies and the food value chain. Processing machines, storage methods, technologicalization, logistics and control of environmental factors, packaging, use of raw materials and energy, losses in the food value chain.
Teaching material: textbooks, lectures by teachers and scientific articles.
The course will be taught in sessions, a total of 7 weeks from March to May.
Recommended preparation: Food Processing Operations/Food Engineering 1/Fish Processing Technology 1
Engineering Design Processes (VÉL203M)
Background for design and engineering design process. Conceptual design, need analysis, specifications, boundary conditions and evaluation criteria. Embodiment and detailed design. CAD system and development of computer graphics. Wire frame model, surface and solid models. Design for reliability, safety and environmental protection.
Mechatronics (VÉL205M)
Mechanical systems and mechatronics system elements. Mechanism, motors, drives, motion converters, sensors and transducers. Signal processing and microprocessor.
Computer Aided Design (VÉL206M)
In this course students are introduced to the basic concepts and methods for parametric representation of curves such as the Bezier-, Hermite- and NURBS curves. Students will learn about the methods for representing three-dimensional wireframe-, solid- and surface models. The course will cover the use of parameters when developing and creating three-dimensional modeling, the creation of assembly drawings using mating operators and how different engineering software solutions can communicate.
The course provides a good fundamental overview of the available engineering software solutions – their advantages and limitations – and the students will learn about the current trends in their field, e.g. in the analysis, simulation, prototyping and manufacturing. The current trends will be indroduced through guest lectures, company visits and a mini-seminar where the students write articles and present new and exciting research or new techniques (based on peer-review papers).
Concurrently with the lectures, students work on an unstructured engineering project where they will engineer and build a working prototype, write the results in a report and present the results.
Plastic, Metals and Fibre Composites (VÉL213F)
The objective of the course is to teach the principles of design with plastic, metals and fibre composites. The course includes topics such as material properties of plastics and manufacturing methods, fiber composites, sandwiches and calculations of stress and strains in structures made of composites materials.
Thesis (VÉL217F)
Students are given the opportunity to suggest an idea for project topics in consultation with faculty members.
Direct Geothermal Utilization (VÉL218F)
The main topics of the course are:
- Energy usage in Iceland, a broad overview.
- House heating and district heating systems:
- Thermodynamics of house heating and energy flow in houses. Heat loss and heat transfer from radiators.
- Minimum requirements for indoor temperature levels, related to quality of living.
- District heating connections to houses, obligatory equipment, heat exchangers.
- Mathematical representation of district heating systems, steady and unsteady operation.
- Base load for district heating suppliers, its determination based on weather data.
- Swimming pools.
- Greenhouses and heating of soil.
- Snow melting and the use of heat in industry.
- Fish farming.
- Heat pumps.
Fish Processing Technology 2 (VÉL601M)
The main goal of the course is to train students to use their knowledge from various fields in mechanical engineering to organize and design fish processing plants and companies. Design requirements and design of production processes for fresh fish, frozen fish, dried fish, fish meal and canning plants. Production management, productivity estimates, quality control, wage structure, etc. for such companies. Heat and mass balances, steady and time dependent heat transfer, utilization of Heisler- and Mollier charts.
Exercises: Fish processing company or certain processes are analyzed and/or redesigned.
Introduction to Nanotechnology (EÐL624M)
Nanostructures and Nanomaterials, Nanoparticles, Nanowires, Thin films, thin film growth, growth modes, transport properties. Characterization of nanomaterials, Crystallography,Particle Size Determination, Surface Structure, Scanning Tunneling Microscope, Atomic Force Microscope, X-ray diffraction (XRD), X-ray reflectometry (XRR), Scanning Electron Microscpe (SEM), and Transmission Electron Microscopy (TEM). Scaling of transistors, MOSFET, and finFET. Carbon Nanoscructures, Graphene and Carbon nanotubes. Lithography. Nanostructred Ferromagnetism. Nano-optics, Plasmonics, metamaterials, cloaking and invinsibility. Molecular Electronics.
Innovation, Product Development, Marketing (IÐN202M)
An insight into the structure of innovation, product development and marketing and how to use this methodology as a tool of management in industrial companies. Theory and practical methods of innovation, product development and marketing. Training in project management and how to run integrated projects covering those three areas by solving realistic problems.
Field Course in Innovation and Entrepreneurship (II) (IÐN216F)
The course is a continuation of the course "Field Course in Innovation and Entrepreneurship (I)". This part of the course consists of detailed development of the business model related to a particular business opportunity. This work takes place in groups, where cross-disciplinary collaboration, between individuals with a background in business and individuals with a background in a particular technical or professional field related to the relevant opportunity, is emphasized. Projects can originate in an independent business idea or in collaboration with companies that partner with the course. In both cases, the emphasis will be on product or service develepment, built on technical or professional expertise, where the business case of the opportunity and its verification is in the foreground.
Field Course in Innovation and Entrepreneurship (I) (IÐN222F)
The course is taught in two parts, and the expectation is that students register for both parts. The course will cover the practical issues related to innovation and entrepreneurship. It covers the emergence of a business idea and the initial evaluation of the business opportunity, and the development and testing of a business model. This part of the course consists of lectures and case discussions that deal with various aspects of innovation and entrepreneurship: Analysis of business opportunities, evaluation of market size and unit contribution, the management of organizational units that are involved in innovation, financing, and other issues. Students will also tackle projects where they apply the methods taught in the class to isolated tasks in product and business development in both new and existing firms.
Introduction to Systems Biology (LVF601M)
Systems biology is an interdisciplinary field that studies the biological phenomena that emerge from multiple interacting biological elements. Understanding how biological systems change across time is a particular focus of systems biology. In this course, we will prioritize aspects of systems biology relevant to human health and disease.
This course provides an introduction to 1) basic principles in modelling molecular networks, both gene regulatory and metabolic networks; 2) cellular phenomena that support homeostasis like tissue morphogenesis and microbiome resilience, and 3) analysis of molecular patterns found in genomics data at population scale relevant to human disease such as patient classification and biomarker discovery. In this manner, the course covers the three major scales in systems biology: molecules, cells and organisms.
The course activities include reading and interpreting scientific papers, implementation of computational algorithms, working on a research project and presentation of scientific results.
Lectures will comprise of both (1) presentations on foundational concepts and (2) hands-on sessions using Python as the programming language. The course will be taught in English.
Robotics and Computer Vision (RAF614M)
Mathematical foundations of coordinate systems and transformations. Kinematics, direct and reverse solutions. Analysis and control of movements. Trajectories in three-dimensional space and interpolation between programmed trajectory points. Use of computer vision, sensors and end-effectors in robotics. Control and programming of robots. Excercises and simulations.
Science and innovation in medical technology (RAF615M)
This course provides an introduction to the diverse applications of electrical and computer engineering in medicine and medical technology. Students will explore cutting-edge developments in the field through guest lecturers from industry professionals in Iceland who apply engineering methods to solve critical medical challenges. Key topics include signal and image processing in medicine and genetics, signal processing and sensors in relation to sleep and the central nervous system, prosthetics, artificial intelligence, and more. Finally, students will have the opportunity to design their own research projects focusing on applying engineering solutions to address medical challenges. Through this work, students will be introduced to writing research proposals and grant applications, with relevance to both industry and academic settings.
The AI lifecycle (REI603M)
In this course, we study the AI lifecycle, i.e. the productionisation of AI methods.
We explore the following parts of the lifecycle:
- Data collection and preparation
- Feature engineering
- Model training
- Model evaluation
- Model deployment
- Model serving
- Model monitoring
- Model maintenance
Three large projects will be handed out during the semester where students compete to solve AI problems.
Final project (VÉL441L)
- The topic of the Master's thesis must be chosen under the guidance of the supervisor and the Faculty Coordinator of the student. The thesis represents 30 or 60 credits. All Master's student have been assigned to a Faculty Coordinator from the beginning of their studies, who advises the student regarding the organization of the program. If a student does not have a supervisor for the final project, he / she must turn to the Faculty Coordinator for assistance.
- The choice of topic is primarily the responsibility of the student in collaboration with his or her project supervisor. The topic of the project should fall within the student's area of study, i.e. course of study and chosen specialisation.
- The master’s student writes a thesis according to the School’s template and defends it in a master’s defense.
- Final project exam is divided into two parts: Oral examination and open lecture
- Present at the oral exam is the student, supervisor, examiner and members of the Master's committee. The student presents a brief introduction on his / her project. It is important that the objectives and research question(s) are clearly stated, and that main findings and lessons to be drawn from the project are discussed.
- The student delivers a thesis and a project poster.
- According to the rules of the Master's program, all students who intend to graduate from the School of Engineering and Natural Sciences need to give a public lecture on their final project.
- All students graduating from the University of Iceland shall submit an electronic copy of their final Master's thesis to Skemman.is. Skemman is the digital repository for all Icelandic universities and is maintained by the National and University Library.
- According to regulations of University of Iceland all MS thesis should have open access after they have been submitted to Skemman.
Learning Outcomes:
Upon completion of an MS thesis, the student should be able to:
- Formulate engineering design project / research questions
- Use an appropriate theoretical framework to shed light on his / her topic
- Analyze and solve engineering tasks in a specialized field.
- Perform a literature search and a thorough review of the literature.
- Demonstrate initiative and independent creative thinking.
- Use economic methodology to answer a specific research question
- Competently discuss the current knowledge within the field and contribute to it with own research
- Work with results, analyze uncertainties and limitations and interpret results.
- Assess the scope of a research project and plan the work accordingly
- Effectively display results and provide logical reasoning and relate results to the state of knowledge.
Selected Topics in Mechanical Engineering (VÉL049F)
Lectures on and study of selected topics in current research and recent development in the field of Mechanical engineering. Topics may vary.
Students contact the teacher and the chair of department regarding registration for the course.
Computational Fluid Dynamics (VÉL215F)
The main purpose is to develop methods of predicting numerical solutions in fluid mechanics and heat transfer. Especially of predicting boundary layer phenomena and modelling of turbulence transport properties. Both finite volume and finite difference methods are demonstrated. Solution of non-linear equations and stability criterium. Emphasis is laid on solution of practical problems.
The course is taught every other year on odd numbered years.
High Performance Computing (REI204M)
Design of parallel computers and parallel programming models. Shared memory architecture. Message passing and distributed memory architecture. Parallel programming of computer clusters using MPI and multicore programming using OpenMP. Parallel algorithms for sorting, searching, linear algebra, and various graph problems.
Course topics will be very similar like HPC in Fall 2019:
http://www.morrisriedel.de/hpc-course-fall-2019
Positioning in the Field of High-Performance Computing (HPC)
- Consists of techniques for programming & using large-scale HPC Systems
- Approach: Get a broad understanding of what HPC is and what can be done
- Goal: Train general HPC techniques and systems and selected details of domain-specific applications
Course Motivation
Parallel processing and distributed computing
- Matured over the past three decades
- Both emerged as a well-developed field in computer science
- Still a lot of innovation, e.g. from hardware/software
‘Scientific computing‘ with Maple, Matlab, etc.
- Performed on small (‘serial‘) computing machines like Desktop PCs or Laptops
- An increasing number of cores enables ‘better scientific computing‘ today
- Good for small & fewer complex applications, quickly reach memory limits
‘Advanced scientific computing‘
- Used with computational simulations and large-scale machine & deep learning
- Performed on large parallel computers; often scientific domain-specific approaches
- Use orders of magnitude multi-core chips & large memory & specific many-core chips
- Enables ‘simulations of reality‘ - often based on known physical laws and numerical methods
Selected Topics in Mechanical Engineering (VÉL072M)
Lectures on and study of selected topics in current research and recent development in the field of Mechanical engineering. Topics may vary.
Students contact the teacher and the chair of department regarding registration for the course.
Fish Processing Technology 1 (MAT508M)
The role of the fish industry in the Icelandic economy. Fish as raw material, its composition, physical and chemical properties. Fish stocks, fishing gear, selectivity. Storage methods on board and after landing. Processing methods, production process and processing equipment for cooling, superchilling, freezing, salting, drying, canning and shell process. Energy and mass balance for each step in the process and the whole process.
- Fall
- Fall
- VÉL114FGeothermal Power PlantsElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse Description
The main topics of the course are:
- Geothermal power plants worldwide and in Iceland.
- Thermodynamics in flash power plants, power cycles.
- Steam separators, condensers, incondensable gases, cooling.
- Alternative power cycles, ORC and Kalina power plants.
- Co-production of heat and power.
- Mechanical design of pipe systems, especially for steam and steam gathering.
- Scaling and corrosion. Environmental effects related to geothermal utilization.
- Economics and cost, both in building phase and operation.
- Exergy and its relation to environmental conditions.
- Energy and exergy analysis, Sankey og Grassmann charts.
- Flow of energy cost, thermo-economics and estimation of net cost in improving parts of energy systems.
- Production control and equipment for process regulation.
Face-to-face learningPrerequisitesCourse DescriptionStudents are given the opportunity to suggest an idea for project topics in consultation with faculty members.
Self-studyPrerequisitesCourse DescriptionThe main topics of the course are:
- Geothermal wells, different types and drilling methods.
- Well casings and mechanical design of geothermal wells.
- Well cementing, work procedures and standards.
- Wellheads and their design. Load on wellheads and related security issues.
- Well logging and measurements along wells. Methods for measuring temperature and pressure.
- Well flow initiation and measurement of the mass flow of steam and water. Energy flow from geothermal wells and potential power production capacity.
- Two phase flow of steam and water. Flow in wells and steam gathering pipes on the surface. Flow properties and determination of flow regimes. Pressure variations in two phase flow.
Face-to-face learningPrerequisitesNot taught this semesterVÉL502MFish Processing Technology 1Elective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionThe role of the fish industry in the Icelandic economy. Fish as raw material, its composition, physical and chemical properties. Fish stocks, fishing gear, selectivity. Storage methods on board and after landing. Processing methods, production process and processing equipment for cooling, superchilling, freezing, salting, drying, canning and shell process. Energy and mass balance for each step in the process and the whole process.
Face-to-face learningPrerequisitesNot taught this semesterIÐN101MQuality ManagementElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionOrganization and management systems. The systems approach. Quality management, quality concepts. Historical development of quality management. Quality cost. Quality in manufacturing. x, R, p, c and cusum-chart. Statistical quality control. Tests of hypotheses. Acceptance sampling - OC curves. Inspection planning. Quality systems and quality assurance. Quality handbook and organizing for quality. ISO 9001. Total Quality Management, improvement step by step, motivations theories. Quality tools. Practical assignment: Designing a quality system for a company.
Face-to-face learningPrerequisitesNot taught this semesterIÐN102MComputational IntelligenceElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionBasic aspects of computational intelligence, which is the study of algorithms that improve automatically through experience.
Face-to-face learningThe course is taught if the specified conditions are metPrerequisitesIÐN508MOperations Research 2Elective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionThis course will introduce the student to decision and optimization models in operations research. On completing the course the student will be able to formulate, analyze, and solve mathematical models, which represent real-world problems, and critically interpret their results. The course will cover integer programming and modeling with stochastic programming. The student will become familiar with building mathematical models using Python.
Face-to-face learningPrerequisitesRAF507MMedical Imaging SystemsElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionIntroduction to the instrumentation, physics and signal processing methods used in medical imaging systems from a signal processing perspective. The modalities covered include projection radiography, X-ray computed tomography, nuclear medicine (i.e. SPECT and PET), ultrasound, and magnetic resonance imaging. The primary focus is on the methods required to reconstruct images within each modality, the kind of signals being measured and how these data culminate in an image. Attention will also be given to image quality in each modality, including resolution, contrast, signal-to-noise ratio, and distortion of images.
Face-to-face learningPrerequisitesREI504MCloud Computing and Big DataElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionOverview of high performance computing (HPC) and “Big Data”, HPC environments with computing, network and storage resources, overview of parallel programming. Storage infrastructures and services for Big Data, Big Data analytics, the map-reduce paradigm, structured and unstructured data. Practical exercises: (A) Students will use the Amazon Web Services (AWS) cloud or equivalent to set up a multi-computer web service and an associated multi-computer testing application. (B) Students will get hands on experience of processing large data sets using map-reduce techniques with AWS.
Face-to-face learningPrerequisitesCourse DescriptionAn overview of some of the main concepts, techniques and algorithms in machine learning. Supervised learning and unsupervised learning. Data preprocessing and data visualization. Model evaluation and model selection. Linear regression, nearest neighbors, support vector machines, decision trees and ensemble methods. Deep learning. Cluster analysis and the k-means algorithm. The students implement simple algorithms in Python and learn how to use specialized software packages. At the end of the course the students work on a practical machine learning project.
Face-to-face learningPrerequisitesCourse DescriptionObjectives: This course is to provide an understanding of membrane technology applied in various industries, such as utilities (water and sewer), environmental industry, food industry, pharmaceutical industry, and chemical/biochemical industry.
Topics: (1) Membrane technology as a solution in industries (separation and purification of food, pharmaceutical, and chemical products) and in environments (water and wastewater treatment; air pollution control; nutrients recovery and reuse); (2) Membrane materials, chemical-based synthesis methods, modifications; (3) Membrane physical, chemical, and mechanical properties and characterization; (4) Transport phenomena in membrane processes; (5) Membrane fouling and fouling mitigation; (6) Membrane operation unit (such as microfiltration, ultrafiltration, nanofiltration, reverse osmosis, forward osmosis, pressure retarded osmosis, membrane distillation, electrodialysis, gas separation) and their applications in industries; (7) Hybrid membrane processes and their applications in industries; (8) Membrane system design.
Teaching: Lectures (teaching lecture, tutorial lecture, lab lecture) and a group project. Teaching lectures introduce the fundamentals and advances of membrane technology, the application of membrane technology in industry. Tutorial lectures are provided to discuss calculation questions and solutions with students. Lab lecture is performed in the research lab to demonstrate selected membrane processes and allow students hands-on practice. In the group project, students review literatures of a selected topic relating to advanced membrane technology, write a report, and give an oral presentation.
The course is also suitable for students specializing in other fields than Civil or Environmental Engineering, e.g., Chemical engineering, Industrial Engineering, Mechanical Engineering, Bioengineering, and Food science.
Face-to-face learningPrerequisitesVÉL441LFinal projectMandatory (required) course0A mandatory (required) course for the programme0 ECTS, creditsCourse Description- The topic of the Master's thesis must be chosen under the guidance of the supervisor and the Faculty Coordinator of the student. The thesis represents 30 or 60 credits. All Master's student have been assigned to a Faculty Coordinator from the beginning of their studies, who advises the student regarding the organization of the program. If a student does not have a supervisor for the final project, he / she must turn to the Faculty Coordinator for assistance.
- The choice of topic is primarily the responsibility of the student in collaboration with his or her project supervisor. The topic of the project should fall within the student's area of study, i.e. course of study and chosen specialisation.
- The master’s student writes a thesis according to the School’s template and defends it in a master’s defense.
- Final project exam is divided into two parts: Oral examination and open lecture
- Present at the oral exam is the student, supervisor, examiner and members of the Master's committee. The student presents a brief introduction on his / her project. It is important that the objectives and research question(s) are clearly stated, and that main findings and lessons to be drawn from the project are discussed.
- The student delivers a thesis and a project poster.
- According to the rules of the Master's program, all students who intend to graduate from the School of Engineering and Natural Sciences need to give a public lecture on their final project.
- All students graduating from the University of Iceland shall submit an electronic copy of their final Master's thesis to Skemman.is. Skemman is the digital repository for all Icelandic universities and is maintained by the National and University Library.
- According to regulations of University of Iceland all MS thesis should have open access after they have been submitted to Skemman.
Learning Outcomes:
Upon completion of an MS thesis, the student should be able to:
- Formulate engineering design project / research questions
- Use an appropriate theoretical framework to shed light on his / her topic
- Analyze and solve engineering tasks in a specialized field.
- Perform a literature search and a thorough review of the literature.
- Demonstrate initiative and independent creative thinking.
- Use economic methodology to answer a specific research question
- Competently discuss the current knowledge within the field and contribute to it with own research
- Work with results, analyze uncertainties and limitations and interpret results.
- Assess the scope of a research project and plan the work accordingly
- Effectively display results and provide logical reasoning and relate results to the state of knowledge.
Self-studyPrerequisitesPart of the total project/thesis creditsVÉL049FSelected Topics in Mechanical EngineeringElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionLectures on and study of selected topics in current research and recent development in the field of Mechanical engineering. Topics may vary.
Students contact the teacher and the chair of department regarding registration for the course.
Self-studyPrerequisitesNot taught this semesterVÉL102MEnergy Intensive Production ProcessesElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionThe aim of the course is:- To give students overview of processes in materials engineering;- To encourage students to think about feasible ways to utilize renewable energy. The course will cover the industrial processes in some of the larger Icelandic companies, including the production of ferro-alloys, aluminium smelting, rockwool production, recycling of steel, algea and diatomitemining, and production of sodium chlorine, fertilizers, cement. The course will also cover some of the larger material engineering processes that are not in practice in Iceland but may be a feasible option for Icelandic industry. Students will get good overview of the processes, required materials, source of power and power consumption, pollution, products etc. Discussions will be held on the financial background for individual processes, covering aspects such as production cost, profit and the influences of market share changes. Grades are based on 2 larger projects the students work on through the semester. Field trips are an important part of the course.
Face-to-face learningPrerequisitesVÉL103MComputational Structural MechanicsElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionThe aim of this course is to give students an exposure to the theoretical basis of the finite element method and its implementation principles. Furthermore, to introduce the use of available finite element application software for solving real-life engineering problems.
The course covers such topics as: stiffness matrices, elements stiffness matrix, system stiffness matrix, local and global stiffness, shape functions, isoparametric formulation and numerical integration. Various elements are studied, such as, trusses and beams, plane elements, 3D elements, plates and shells. Students mostly solve problems in solid mechanics (stress analysis) but can choose to work on a design project in other areas, such as vibrations or heat transfer.
The course includes class lectures and work sessions where students solve problems, both in python (can also choose matlab) and in the commercial software Ansys, under the supervison of the instructor. There is extensive use of Python (Matlab) and Ansys in solving homework problems and semester projects.
Face-to-face learningPrerequisitesCourse DescriptionSpectral and wavelet analysis. Linear and nonlinear systems. Envelope and cepstrum analysis Measurement, identification and response problems. Vibrations of continuous systems. Formulation of finite element model for analysis of dynamic problems. Fault diagnostic and machine condition monitoring.
Face-to-face learningPrerequisitesVÉL113FDesign OptimizationElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionOptimum design concepts. Fundamentals of linear and nonlinear programming, constrained and unconstrained optimum design problems. Simulated annealing and genetic algorithms. Project and applications to realistic engineering design problems.
Face-to-face learningPrerequisitesCourse DescriptionBasic thermodynamic and electrochemical principles that cause corrosion. Procedures of electrochemical measurements used to investigate corrosion behavior. Methods of corrosion protection and prevention, materials selection and design.
The course is taught every other year on even numbered years.
Face-to-face learningThe course is taught if the specified conditions are metPrerequisitesEÐL520MCondensed Matter Physics 1Elective course8Free elective course within the programme8 ECTS, creditsCourse DescriptionThe course is an introduction to some basic concepts of condensed matter physics. Curriculum: Chemical bonds, crystal structure, crystal symmetry, the reciprocal lattice. Vibrational modes of crystals, phonons, specific heat, thermal conductivity. The free electron model, band structure of condensed matter, effective mass. Metals, insulators and semiconductors. The course includes three labs.
Face-to-face learningPrerequisitesIÐN113FTime Series AnalysisElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionARMAX and other similar time series models. Non-stationary time series. Correlation and spectral analysis. Parameter estimation, parametric and non-parametric approaches, Least Squares and Maximum Likelihood. Model validation methods. Models with time dependent parameters. Numerical methods for minimization. Outlier detection and interpolation. Introduction to nonlinear time series models. Discrete state space models. Discrete state space models. Extensive use of MATLAB, especially the System Identification Toolbox.
Distance learningSelf-studyPrerequisitesJEÐ503MContinuum Mechanics and Heat TransferElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionObjectives: To introduce continuum mechanics, fluid dynamics and heat transfer and their application to problems in physics and geophysics. I. Stress and strain, stress fields, stress tensor, bending of plates, models of material behaviour: elastic, viscous, plastic materials. II. Fluids, viscous fluids, laminar and turbulent flow, equation of continuity, Navier-Stokes equation. III. Heat transfer: Heat conduction, convection, advection and geothermal resources. Examples and problems from various branches of physics will be studied, particularly from geophysics.
Teaching statement: To do well in this course, students should actively participate in the discussions, attend lectures, give student presentations and deliver the problem sets assigned in the course. Students will gain knowledge through the lectures, but it is necessary to do the exercises to understand and train the use of the concepts. The exercises are intergrated in the text of the book, it is recommended to do them while reading the text. Instructors will strive to make the concepts and terminology accessible, but it is expected that students study independently and ask questions if something is unclear. In order to improve the course and its content, it is appreciated that students participate in the course evaluation, both the mid-term and the end of term course evaluation.
Face-to-face learningPrerequisitesSTÆ312MApplied Linear Statistical ModelsElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionThe course focuses on simple and multiple linear regression as well as analysis of variance (ANOVA), analysis of covariance (ANCOVA) and binomial regression. The course is a natural continuation of a typical introductory course in statistics taught in various departments of the university.
We will discuss methods for estimating parameters in linear models, how to construct confidence intervals and test hypotheses for the parameters, which assumptions need to hold for applying the models and what to do when they are not met.
Students will work on projects using the statistical software R.
Face-to-face learningPrerequisitesVON001FThesis skills: project management, writing skills and presentationElective course4Free elective course within the programme4 ECTS, creditsCourse DescriptionIntroduction to the scientific method. Ethics of science and within the university community.
The role of the student, advisors and external examiner. Effective and honest communications.
Conducting a literature review, using bibliographic databases and reference handling. Thesis structure, formulating research questions, writing and argumentation. How scientific writing differs from general purpose writing. Writing a MS study plan and proposal. Practical skills for presenting tables and figures, layout, fonts and colors. Presentation skills. Project management for a thesis, how to divide a large project into smaller tasks, setting a work plan and following a timeline. Life after graduate school and being employable.Face-to-face learningOnline learningPrerequisites- Spring 2
Course DescriptionCourse Description:
Objective: That students can evaluate food processes and calculate the main variables in different unit operations, plan and control food processes. To make students more capable of making decisions about changes in manufacture and transport processes.
In the lectures, the main food processes are reviewed:
- The effect of holding time and temperature in manufacturing processes and water content and water activity on the quality and properties of foods
- Processing/preservation methods such as chilling, superchilling, freezing and thawing, salting, smoking, heating and canning, drying, evaporation, separation and fermentation. Use of steam tables, enthalpy- and Mollier diagrams.
- Process flow diagrams/charts by process steps, material flow and balance calculations and risk analysis.
- Processing and packaging equipment and packaging for different foods
- Main parameters of production control.
- Storage conditions (light, humidity, temperature, air composition, etc.) and key factors affecting changes in food during storage, transportation and sale/distribution of food.
- Design considerations for food processing companies and the food value chain. Processing machines, storage methods, technologicalization, logistics and control of environmental factors, packaging, use of raw materials and energy, losses in the food value chain.
Teaching material: textbooks, lectures by teachers and scientific articles.
The course will be taught in sessions, a total of 7 weeks from March to May.
Recommended preparation: Food Processing Operations/Food Engineering 1/Fish Processing Technology 1
Face-to-face learningPrerequisitesCourse taught second half of the semesterNot taught this semesterVÉL203MEngineering Design ProcessesElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionBackground for design and engineering design process. Conceptual design, need analysis, specifications, boundary conditions and evaluation criteria. Embodiment and detailed design. CAD system and development of computer graphics. Wire frame model, surface and solid models. Design for reliability, safety and environmental protection.
Face-to-face learningPrerequisitesCourse DescriptionMechanical systems and mechatronics system elements. Mechanism, motors, drives, motion converters, sensors and transducers. Signal processing and microprocessor.
Face-to-face learningPrerequisitesVÉL206MComputer Aided DesignElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionIn this course students are introduced to the basic concepts and methods for parametric representation of curves such as the Bezier-, Hermite- and NURBS curves. Students will learn about the methods for representing three-dimensional wireframe-, solid- and surface models. The course will cover the use of parameters when developing and creating three-dimensional modeling, the creation of assembly drawings using mating operators and how different engineering software solutions can communicate.
The course provides a good fundamental overview of the available engineering software solutions – their advantages and limitations – and the students will learn about the current trends in their field, e.g. in the analysis, simulation, prototyping and manufacturing. The current trends will be indroduced through guest lectures, company visits and a mini-seminar where the students write articles and present new and exciting research or new techniques (based on peer-review papers).
Concurrently with the lectures, students work on an unstructured engineering project where they will engineer and build a working prototype, write the results in a report and present the results.
Face-to-face learningPrerequisitesVÉL213FPlastic, Metals and Fibre CompositesElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionThe objective of the course is to teach the principles of design with plastic, metals and fibre composites. The course includes topics such as material properties of plastics and manufacturing methods, fiber composites, sandwiches and calculations of stress and strains in structures made of composites materials.
Face-to-face learningPrerequisitesCourse DescriptionStudents are given the opportunity to suggest an idea for project topics in consultation with faculty members.
Self-studyThe course is taught if the specified conditions are metPrerequisitesVÉL218FDirect Geothermal UtilizationElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionThe main topics of the course are:
- Energy usage in Iceland, a broad overview.
- House heating and district heating systems:
- Thermodynamics of house heating and energy flow in houses. Heat loss and heat transfer from radiators.
- Minimum requirements for indoor temperature levels, related to quality of living.
- District heating connections to houses, obligatory equipment, heat exchangers.
- Mathematical representation of district heating systems, steady and unsteady operation.
- Base load for district heating suppliers, its determination based on weather data.
- Swimming pools.
- Greenhouses and heating of soil.
- Snow melting and the use of heat in industry.
- Fish farming.
- Heat pumps.
Face-to-face learningPrerequisitesNot taught this semesterVÉL601MFish Processing Technology 2Elective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionThe main goal of the course is to train students to use their knowledge from various fields in mechanical engineering to organize and design fish processing plants and companies. Design requirements and design of production processes for fresh fish, frozen fish, dried fish, fish meal and canning plants. Production management, productivity estimates, quality control, wage structure, etc. for such companies. Heat and mass balances, steady and time dependent heat transfer, utilization of Heisler- and Mollier charts.
Exercises: Fish processing company or certain processes are analyzed and/or redesigned.
Face-to-face learningPrerequisitesNot taught this semesterEÐL624MIntroduction to NanotechnologyElective course8Free elective course within the programme8 ECTS, creditsCourse DescriptionNanostructures and Nanomaterials, Nanoparticles, Nanowires, Thin films, thin film growth, growth modes, transport properties. Characterization of nanomaterials, Crystallography,Particle Size Determination, Surface Structure, Scanning Tunneling Microscope, Atomic Force Microscope, X-ray diffraction (XRD), X-ray reflectometry (XRR), Scanning Electron Microscpe (SEM), and Transmission Electron Microscopy (TEM). Scaling of transistors, MOSFET, and finFET. Carbon Nanoscructures, Graphene and Carbon nanotubes. Lithography. Nanostructred Ferromagnetism. Nano-optics, Plasmonics, metamaterials, cloaking and invinsibility. Molecular Electronics.
Face-to-face learningPrerequisitesNot taught this semesterIÐN202MInnovation, Product Development, MarketingElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionAn insight into the structure of innovation, product development and marketing and how to use this methodology as a tool of management in industrial companies. Theory and practical methods of innovation, product development and marketing. Training in project management and how to run integrated projects covering those three areas by solving realistic problems.
Face-to-face learningPrerequisitesIÐN216FField Course in Innovation and Entrepreneurship (II)Elective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionThe course is a continuation of the course "Field Course in Innovation and Entrepreneurship (I)". This part of the course consists of detailed development of the business model related to a particular business opportunity. This work takes place in groups, where cross-disciplinary collaboration, between individuals with a background in business and individuals with a background in a particular technical or professional field related to the relevant opportunity, is emphasized. Projects can originate in an independent business idea or in collaboration with companies that partner with the course. In both cases, the emphasis will be on product or service develepment, built on technical or professional expertise, where the business case of the opportunity and its verification is in the foreground.
Face-to-face learningPrerequisitesCourse taught second half of the semesterIÐN222FField Course in Innovation and Entrepreneurship (I)Elective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionThe course is taught in two parts, and the expectation is that students register for both parts. The course will cover the practical issues related to innovation and entrepreneurship. It covers the emergence of a business idea and the initial evaluation of the business opportunity, and the development and testing of a business model. This part of the course consists of lectures and case discussions that deal with various aspects of innovation and entrepreneurship: Analysis of business opportunities, evaluation of market size and unit contribution, the management of organizational units that are involved in innovation, financing, and other issues. Students will also tackle projects where they apply the methods taught in the class to isolated tasks in product and business development in both new and existing firms.
Face-to-face learningPrerequisitesCourse taught first half of the semesterNot taught this semesterLVF601MIntroduction to Systems BiologyElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionSystems biology is an interdisciplinary field that studies the biological phenomena that emerge from multiple interacting biological elements. Understanding how biological systems change across time is a particular focus of systems biology. In this course, we will prioritize aspects of systems biology relevant to human health and disease.
This course provides an introduction to 1) basic principles in modelling molecular networks, both gene regulatory and metabolic networks; 2) cellular phenomena that support homeostasis like tissue morphogenesis and microbiome resilience, and 3) analysis of molecular patterns found in genomics data at population scale relevant to human disease such as patient classification and biomarker discovery. In this manner, the course covers the three major scales in systems biology: molecules, cells and organisms.
The course activities include reading and interpreting scientific papers, implementation of computational algorithms, working on a research project and presentation of scientific results.
Lectures will comprise of both (1) presentations on foundational concepts and (2) hands-on sessions using Python as the programming language. The course will be taught in English.
Face-to-face learningPrerequisitesRAF614MRobotics and Computer VisionElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionMathematical foundations of coordinate systems and transformations. Kinematics, direct and reverse solutions. Analysis and control of movements. Trajectories in three-dimensional space and interpolation between programmed trajectory points. Use of computer vision, sensors and end-effectors in robotics. Control and programming of robots. Excercises and simulations.
Face-to-face learningPrerequisitesRAF615MScience and innovation in medical technologyElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionThis course provides an introduction to the diverse applications of electrical and computer engineering in medicine and medical technology. Students will explore cutting-edge developments in the field through guest lecturers from industry professionals in Iceland who apply engineering methods to solve critical medical challenges. Key topics include signal and image processing in medicine and genetics, signal processing and sensors in relation to sleep and the central nervous system, prosthetics, artificial intelligence, and more. Finally, students will have the opportunity to design their own research projects focusing on applying engineering solutions to address medical challenges. Through this work, students will be introduced to writing research proposals and grant applications, with relevance to both industry and academic settings.
Face-to-face learningPrerequisitesCourse DescriptionIn this course, we study the AI lifecycle, i.e. the productionisation of AI methods.
We explore the following parts of the lifecycle:
- Data collection and preparation
- Feature engineering
- Model training
- Model evaluation
- Model deployment
- Model serving
- Model monitoring
- Model maintenance
Three large projects will be handed out during the semester where students compete to solve AI problems.Face-to-face learningPrerequisitesVÉL441LFinal projectMandatory (required) course0A mandatory (required) course for the programme0 ECTS, creditsCourse Description- The topic of the Master's thesis must be chosen under the guidance of the supervisor and the Faculty Coordinator of the student. The thesis represents 30 or 60 credits. All Master's student have been assigned to a Faculty Coordinator from the beginning of their studies, who advises the student regarding the organization of the program. If a student does not have a supervisor for the final project, he / she must turn to the Faculty Coordinator for assistance.
- The choice of topic is primarily the responsibility of the student in collaboration with his or her project supervisor. The topic of the project should fall within the student's area of study, i.e. course of study and chosen specialisation.
- The master’s student writes a thesis according to the School’s template and defends it in a master’s defense.
- Final project exam is divided into two parts: Oral examination and open lecture
- Present at the oral exam is the student, supervisor, examiner and members of the Master's committee. The student presents a brief introduction on his / her project. It is important that the objectives and research question(s) are clearly stated, and that main findings and lessons to be drawn from the project are discussed.
- The student delivers a thesis and a project poster.
- According to the rules of the Master's program, all students who intend to graduate from the School of Engineering and Natural Sciences need to give a public lecture on their final project.
- All students graduating from the University of Iceland shall submit an electronic copy of their final Master's thesis to Skemman.is. Skemman is the digital repository for all Icelandic universities and is maintained by the National and University Library.
- According to regulations of University of Iceland all MS thesis should have open access after they have been submitted to Skemman.
Learning Outcomes:
Upon completion of an MS thesis, the student should be able to:
- Formulate engineering design project / research questions
- Use an appropriate theoretical framework to shed light on his / her topic
- Analyze and solve engineering tasks in a specialized field.
- Perform a literature search and a thorough review of the literature.
- Demonstrate initiative and independent creative thinking.
- Use economic methodology to answer a specific research question
- Competently discuss the current knowledge within the field and contribute to it with own research
- Work with results, analyze uncertainties and limitations and interpret results.
- Assess the scope of a research project and plan the work accordingly
- Effectively display results and provide logical reasoning and relate results to the state of knowledge.
Self-studyPrerequisitesPart of the total project/thesis creditsVÉL049FSelected Topics in Mechanical EngineeringElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionLectures on and study of selected topics in current research and recent development in the field of Mechanical engineering. Topics may vary.
Students contact the teacher and the chair of department regarding registration for the course.
Self-studyPrerequisitesVÉL215FComputational Fluid DynamicsElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionThe main purpose is to develop methods of predicting numerical solutions in fluid mechanics and heat transfer. Especially of predicting boundary layer phenomena and modelling of turbulence transport properties. Both finite volume and finite difference methods are demonstrated. Solution of non-linear equations and stability criterium. Emphasis is laid on solution of practical problems.
The course is taught every other year on odd numbered years.
Face-to-face learningThe course is taught if the specified conditions are metPrerequisitesREI204MHigh Performance ComputingElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionDesign of parallel computers and parallel programming models. Shared memory architecture. Message passing and distributed memory architecture. Parallel programming of computer clusters using MPI and multicore programming using OpenMP. Parallel algorithms for sorting, searching, linear algebra, and various graph problems.
Course topics will be very similar like HPC in Fall 2019:
http://www.morrisriedel.de/hpc-course-fall-2019
Positioning in the Field of High-Performance Computing (HPC)
- Consists of techniques for programming & using large-scale HPC Systems
- Approach: Get a broad understanding of what HPC is and what can be done
- Goal: Train general HPC techniques and systems and selected details of domain-specific applications
Course Motivation
Parallel processing and distributed computing
- Matured over the past three decades
- Both emerged as a well-developed field in computer science
- Still a lot of innovation, e.g. from hardware/software
‘Scientific computing‘ with Maple, Matlab, etc.
- Performed on small (‘serial‘) computing machines like Desktop PCs or Laptops
- An increasing number of cores enables ‘better scientific computing‘ today
- Good for small & fewer complex applications, quickly reach memory limits
‘Advanced scientific computing‘
- Used with computational simulations and large-scale machine & deep learning
- Performed on large parallel computers; often scientific domain-specific approaches
- Use orders of magnitude multi-core chips & large memory & specific many-core chips
- Enables ‘simulations of reality‘ - often based on known physical laws and numerical methods
Face-to-face learningPrerequisitesVÉL072MSelected Topics in Mechanical EngineeringElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionLectures on and study of selected topics in current research and recent development in the field of Mechanical engineering. Topics may vary.
Students contact the teacher and the chair of department regarding registration for the course.
Self-studyPrerequisites- Year unspecified
MAT508MFish Processing Technology 1Elective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionThe role of the fish industry in the Icelandic economy. Fish as raw material, its composition, physical and chemical properties. Fish stocks, fishing gear, selectivity. Storage methods on board and after landing. Processing methods, production process and processing equipment for cooling, superchilling, freezing, salting, drying, canning and shell process. Energy and mass balance for each step in the process and the whole process.
Face-to-face learningPrerequisitesYear unspecified- Fall
- Fall
- VÉL114FGeothermal Power PlantsElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse Description
The main topics of the course are:
- Geothermal power plants worldwide and in Iceland.
- Thermodynamics in flash power plants, power cycles.
- Steam separators, condensers, incondensable gases, cooling.
- Alternative power cycles, ORC and Kalina power plants.
- Co-production of heat and power.
- Mechanical design of pipe systems, especially for steam and steam gathering.
- Scaling and corrosion. Environmental effects related to geothermal utilization.
- Economics and cost, both in building phase and operation.
- Exergy and its relation to environmental conditions.
- Energy and exergy analysis, Sankey og Grassmann charts.
- Flow of energy cost, thermo-economics and estimation of net cost in improving parts of energy systems.
- Production control and equipment for process regulation.
Face-to-face learningPrerequisitesCourse DescriptionStudents are given the opportunity to suggest an idea for project topics in consultation with faculty members.
Self-studyPrerequisitesCourse DescriptionThe main topics of the course are:
- Geothermal wells, different types and drilling methods.
- Well casings and mechanical design of geothermal wells.
- Well cementing, work procedures and standards.
- Wellheads and their design. Load on wellheads and related security issues.
- Well logging and measurements along wells. Methods for measuring temperature and pressure.
- Well flow initiation and measurement of the mass flow of steam and water. Energy flow from geothermal wells and potential power production capacity.
- Two phase flow of steam and water. Flow in wells and steam gathering pipes on the surface. Flow properties and determination of flow regimes. Pressure variations in two phase flow.
Face-to-face learningPrerequisitesNot taught this semesterVÉL502MFish Processing Technology 1Elective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionThe role of the fish industry in the Icelandic economy. Fish as raw material, its composition, physical and chemical properties. Fish stocks, fishing gear, selectivity. Storage methods on board and after landing. Processing methods, production process and processing equipment for cooling, superchilling, freezing, salting, drying, canning and shell process. Energy and mass balance for each step in the process and the whole process.
Face-to-face learningPrerequisitesNot taught this semesterIÐN101MQuality ManagementElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionOrganization and management systems. The systems approach. Quality management, quality concepts. Historical development of quality management. Quality cost. Quality in manufacturing. x, R, p, c and cusum-chart. Statistical quality control. Tests of hypotheses. Acceptance sampling - OC curves. Inspection planning. Quality systems and quality assurance. Quality handbook and organizing for quality. ISO 9001. Total Quality Management, improvement step by step, motivations theories. Quality tools. Practical assignment: Designing a quality system for a company.
Face-to-face learningPrerequisitesNot taught this semesterIÐN102MComputational IntelligenceElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionBasic aspects of computational intelligence, which is the study of algorithms that improve automatically through experience.
Face-to-face learningThe course is taught if the specified conditions are metPrerequisitesIÐN508MOperations Research 2Elective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionThis course will introduce the student to decision and optimization models in operations research. On completing the course the student will be able to formulate, analyze, and solve mathematical models, which represent real-world problems, and critically interpret their results. The course will cover integer programming and modeling with stochastic programming. The student will become familiar with building mathematical models using Python.
Face-to-face learningPrerequisitesRAF507MMedical Imaging SystemsElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionIntroduction to the instrumentation, physics and signal processing methods used in medical imaging systems from a signal processing perspective. The modalities covered include projection radiography, X-ray computed tomography, nuclear medicine (i.e. SPECT and PET), ultrasound, and magnetic resonance imaging. The primary focus is on the methods required to reconstruct images within each modality, the kind of signals being measured and how these data culminate in an image. Attention will also be given to image quality in each modality, including resolution, contrast, signal-to-noise ratio, and distortion of images.
Face-to-face learningPrerequisitesREI504MCloud Computing and Big DataElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionOverview of high performance computing (HPC) and “Big Data”, HPC environments with computing, network and storage resources, overview of parallel programming. Storage infrastructures and services for Big Data, Big Data analytics, the map-reduce paradigm, structured and unstructured data. Practical exercises: (A) Students will use the Amazon Web Services (AWS) cloud or equivalent to set up a multi-computer web service and an associated multi-computer testing application. (B) Students will get hands on experience of processing large data sets using map-reduce techniques with AWS.
Face-to-face learningPrerequisitesCourse DescriptionAn overview of some of the main concepts, techniques and algorithms in machine learning. Supervised learning and unsupervised learning. Data preprocessing and data visualization. Model evaluation and model selection. Linear regression, nearest neighbors, support vector machines, decision trees and ensemble methods. Deep learning. Cluster analysis and the k-means algorithm. The students implement simple algorithms in Python and learn how to use specialized software packages. At the end of the course the students work on a practical machine learning project.
Face-to-face learningPrerequisitesCourse DescriptionObjectives: This course is to provide an understanding of membrane technology applied in various industries, such as utilities (water and sewer), environmental industry, food industry, pharmaceutical industry, and chemical/biochemical industry.
Topics: (1) Membrane technology as a solution in industries (separation and purification of food, pharmaceutical, and chemical products) and in environments (water and wastewater treatment; air pollution control; nutrients recovery and reuse); (2) Membrane materials, chemical-based synthesis methods, modifications; (3) Membrane physical, chemical, and mechanical properties and characterization; (4) Transport phenomena in membrane processes; (5) Membrane fouling and fouling mitigation; (6) Membrane operation unit (such as microfiltration, ultrafiltration, nanofiltration, reverse osmosis, forward osmosis, pressure retarded osmosis, membrane distillation, electrodialysis, gas separation) and their applications in industries; (7) Hybrid membrane processes and their applications in industries; (8) Membrane system design.
Teaching: Lectures (teaching lecture, tutorial lecture, lab lecture) and a group project. Teaching lectures introduce the fundamentals and advances of membrane technology, the application of membrane technology in industry. Tutorial lectures are provided to discuss calculation questions and solutions with students. Lab lecture is performed in the research lab to demonstrate selected membrane processes and allow students hands-on practice. In the group project, students review literatures of a selected topic relating to advanced membrane technology, write a report, and give an oral presentation.
The course is also suitable for students specializing in other fields than Civil or Environmental Engineering, e.g., Chemical engineering, Industrial Engineering, Mechanical Engineering, Bioengineering, and Food science.
Face-to-face learningPrerequisitesVÉL441LFinal projectMandatory (required) course0A mandatory (required) course for the programme0 ECTS, creditsCourse Description- The topic of the Master's thesis must be chosen under the guidance of the supervisor and the Faculty Coordinator of the student. The thesis represents 30 or 60 credits. All Master's student have been assigned to a Faculty Coordinator from the beginning of their studies, who advises the student regarding the organization of the program. If a student does not have a supervisor for the final project, he / she must turn to the Faculty Coordinator for assistance.
- The choice of topic is primarily the responsibility of the student in collaboration with his or her project supervisor. The topic of the project should fall within the student's area of study, i.e. course of study and chosen specialisation.
- The master’s student writes a thesis according to the School’s template and defends it in a master’s defense.
- Final project exam is divided into two parts: Oral examination and open lecture
- Present at the oral exam is the student, supervisor, examiner and members of the Master's committee. The student presents a brief introduction on his / her project. It is important that the objectives and research question(s) are clearly stated, and that main findings and lessons to be drawn from the project are discussed.
- The student delivers a thesis and a project poster.
- According to the rules of the Master's program, all students who intend to graduate from the School of Engineering and Natural Sciences need to give a public lecture on their final project.
- All students graduating from the University of Iceland shall submit an electronic copy of their final Master's thesis to Skemman.is. Skemman is the digital repository for all Icelandic universities and is maintained by the National and University Library.
- According to regulations of University of Iceland all MS thesis should have open access after they have been submitted to Skemman.
Learning Outcomes:
Upon completion of an MS thesis, the student should be able to:
- Formulate engineering design project / research questions
- Use an appropriate theoretical framework to shed light on his / her topic
- Analyze and solve engineering tasks in a specialized field.
- Perform a literature search and a thorough review of the literature.
- Demonstrate initiative and independent creative thinking.
- Use economic methodology to answer a specific research question
- Competently discuss the current knowledge within the field and contribute to it with own research
- Work with results, analyze uncertainties and limitations and interpret results.
- Assess the scope of a research project and plan the work accordingly
- Effectively display results and provide logical reasoning and relate results to the state of knowledge.
Self-studyPrerequisitesPart of the total project/thesis creditsVÉL049FSelected Topics in Mechanical EngineeringElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionLectures on and study of selected topics in current research and recent development in the field of Mechanical engineering. Topics may vary.
Students contact the teacher and the chair of department regarding registration for the course.
Self-studyPrerequisitesNot taught this semesterVÉL102MEnergy Intensive Production ProcessesElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionThe aim of the course is:- To give students overview of processes in materials engineering;- To encourage students to think about feasible ways to utilize renewable energy. The course will cover the industrial processes in some of the larger Icelandic companies, including the production of ferro-alloys, aluminium smelting, rockwool production, recycling of steel, algea and diatomitemining, and production of sodium chlorine, fertilizers, cement. The course will also cover some of the larger material engineering processes that are not in practice in Iceland but may be a feasible option for Icelandic industry. Students will get good overview of the processes, required materials, source of power and power consumption, pollution, products etc. Discussions will be held on the financial background for individual processes, covering aspects such as production cost, profit and the influences of market share changes. Grades are based on 2 larger projects the students work on through the semester. Field trips are an important part of the course.
Face-to-face learningPrerequisitesVÉL103MComputational Structural MechanicsElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionThe aim of this course is to give students an exposure to the theoretical basis of the finite element method and its implementation principles. Furthermore, to introduce the use of available finite element application software for solving real-life engineering problems.
The course covers such topics as: stiffness matrices, elements stiffness matrix, system stiffness matrix, local and global stiffness, shape functions, isoparametric formulation and numerical integration. Various elements are studied, such as, trusses and beams, plane elements, 3D elements, plates and shells. Students mostly solve problems in solid mechanics (stress analysis) but can choose to work on a design project in other areas, such as vibrations or heat transfer.
The course includes class lectures and work sessions where students solve problems, both in python (can also choose matlab) and in the commercial software Ansys, under the supervison of the instructor. There is extensive use of Python (Matlab) and Ansys in solving homework problems and semester projects.
Face-to-face learningPrerequisitesCourse DescriptionSpectral and wavelet analysis. Linear and nonlinear systems. Envelope and cepstrum analysis Measurement, identification and response problems. Vibrations of continuous systems. Formulation of finite element model for analysis of dynamic problems. Fault diagnostic and machine condition monitoring.
Face-to-face learningPrerequisitesVÉL113FDesign OptimizationElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionOptimum design concepts. Fundamentals of linear and nonlinear programming, constrained and unconstrained optimum design problems. Simulated annealing and genetic algorithms. Project and applications to realistic engineering design problems.
Face-to-face learningPrerequisitesCourse DescriptionBasic thermodynamic and electrochemical principles that cause corrosion. Procedures of electrochemical measurements used to investigate corrosion behavior. Methods of corrosion protection and prevention, materials selection and design.
The course is taught every other year on even numbered years.
Face-to-face learningThe course is taught if the specified conditions are metPrerequisitesEÐL520MCondensed Matter Physics 1Elective course8Free elective course within the programme8 ECTS, creditsCourse DescriptionThe course is an introduction to some basic concepts of condensed matter physics. Curriculum: Chemical bonds, crystal structure, crystal symmetry, the reciprocal lattice. Vibrational modes of crystals, phonons, specific heat, thermal conductivity. The free electron model, band structure of condensed matter, effective mass. Metals, insulators and semiconductors. The course includes three labs.
Face-to-face learningPrerequisitesIÐN113FTime Series AnalysisElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionARMAX and other similar time series models. Non-stationary time series. Correlation and spectral analysis. Parameter estimation, parametric and non-parametric approaches, Least Squares and Maximum Likelihood. Model validation methods. Models with time dependent parameters. Numerical methods for minimization. Outlier detection and interpolation. Introduction to nonlinear time series models. Discrete state space models. Discrete state space models. Extensive use of MATLAB, especially the System Identification Toolbox.
Distance learningSelf-studyPrerequisitesJEÐ503MContinuum Mechanics and Heat TransferElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionObjectives: To introduce continuum mechanics, fluid dynamics and heat transfer and their application to problems in physics and geophysics. I. Stress and strain, stress fields, stress tensor, bending of plates, models of material behaviour: elastic, viscous, plastic materials. II. Fluids, viscous fluids, laminar and turbulent flow, equation of continuity, Navier-Stokes equation. III. Heat transfer: Heat conduction, convection, advection and geothermal resources. Examples and problems from various branches of physics will be studied, particularly from geophysics.
Teaching statement: To do well in this course, students should actively participate in the discussions, attend lectures, give student presentations and deliver the problem sets assigned in the course. Students will gain knowledge through the lectures, but it is necessary to do the exercises to understand and train the use of the concepts. The exercises are intergrated in the text of the book, it is recommended to do them while reading the text. Instructors will strive to make the concepts and terminology accessible, but it is expected that students study independently and ask questions if something is unclear. In order to improve the course and its content, it is appreciated that students participate in the course evaluation, both the mid-term and the end of term course evaluation.
Face-to-face learningPrerequisitesSTÆ312MApplied Linear Statistical ModelsElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionThe course focuses on simple and multiple linear regression as well as analysis of variance (ANOVA), analysis of covariance (ANCOVA) and binomial regression. The course is a natural continuation of a typical introductory course in statistics taught in various departments of the university.
We will discuss methods for estimating parameters in linear models, how to construct confidence intervals and test hypotheses for the parameters, which assumptions need to hold for applying the models and what to do when they are not met.
Students will work on projects using the statistical software R.
Face-to-face learningPrerequisitesVON001FThesis skills: project management, writing skills and presentationElective course4Free elective course within the programme4 ECTS, creditsCourse DescriptionIntroduction to the scientific method. Ethics of science and within the university community.
The role of the student, advisors and external examiner. Effective and honest communications.
Conducting a literature review, using bibliographic databases and reference handling. Thesis structure, formulating research questions, writing and argumentation. How scientific writing differs from general purpose writing. Writing a MS study plan and proposal. Practical skills for presenting tables and figures, layout, fonts and colors. Presentation skills. Project management for a thesis, how to divide a large project into smaller tasks, setting a work plan and following a timeline. Life after graduate school and being employable.Face-to-face learningOnline learningPrerequisites- Spring 2
Course DescriptionCourse Description:
Objective: That students can evaluate food processes and calculate the main variables in different unit operations, plan and control food processes. To make students more capable of making decisions about changes in manufacture and transport processes.
In the lectures, the main food processes are reviewed:
- The effect of holding time and temperature in manufacturing processes and water content and water activity on the quality and properties of foods
- Processing/preservation methods such as chilling, superchilling, freezing and thawing, salting, smoking, heating and canning, drying, evaporation, separation and fermentation. Use of steam tables, enthalpy- and Mollier diagrams.
- Process flow diagrams/charts by process steps, material flow and balance calculations and risk analysis.
- Processing and packaging equipment and packaging for different foods
- Main parameters of production control.
- Storage conditions (light, humidity, temperature, air composition, etc.) and key factors affecting changes in food during storage, transportation and sale/distribution of food.
- Design considerations for food processing companies and the food value chain. Processing machines, storage methods, technologicalization, logistics and control of environmental factors, packaging, use of raw materials and energy, losses in the food value chain.
Teaching material: textbooks, lectures by teachers and scientific articles.
The course will be taught in sessions, a total of 7 weeks from March to May.
Recommended preparation: Food Processing Operations/Food Engineering 1/Fish Processing Technology 1
Face-to-face learningPrerequisitesCourse taught second half of the semesterNot taught this semesterVÉL203MEngineering Design ProcessesElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionBackground for design and engineering design process. Conceptual design, need analysis, specifications, boundary conditions and evaluation criteria. Embodiment and detailed design. CAD system and development of computer graphics. Wire frame model, surface and solid models. Design for reliability, safety and environmental protection.
Face-to-face learningPrerequisitesCourse DescriptionMechanical systems and mechatronics system elements. Mechanism, motors, drives, motion converters, sensors and transducers. Signal processing and microprocessor.
Face-to-face learningPrerequisitesVÉL206MComputer Aided DesignElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionIn this course students are introduced to the basic concepts and methods for parametric representation of curves such as the Bezier-, Hermite- and NURBS curves. Students will learn about the methods for representing three-dimensional wireframe-, solid- and surface models. The course will cover the use of parameters when developing and creating three-dimensional modeling, the creation of assembly drawings using mating operators and how different engineering software solutions can communicate.
The course provides a good fundamental overview of the available engineering software solutions – their advantages and limitations – and the students will learn about the current trends in their field, e.g. in the analysis, simulation, prototyping and manufacturing. The current trends will be indroduced through guest lectures, company visits and a mini-seminar where the students write articles and present new and exciting research or new techniques (based on peer-review papers).
Concurrently with the lectures, students work on an unstructured engineering project where they will engineer and build a working prototype, write the results in a report and present the results.
Face-to-face learningPrerequisitesVÉL213FPlastic, Metals and Fibre CompositesElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionThe objective of the course is to teach the principles of design with plastic, metals and fibre composites. The course includes topics such as material properties of plastics and manufacturing methods, fiber composites, sandwiches and calculations of stress and strains in structures made of composites materials.
Face-to-face learningPrerequisitesCourse DescriptionStudents are given the opportunity to suggest an idea for project topics in consultation with faculty members.
Self-studyThe course is taught if the specified conditions are metPrerequisitesVÉL218FDirect Geothermal UtilizationElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionThe main topics of the course are:
- Energy usage in Iceland, a broad overview.
- House heating and district heating systems:
- Thermodynamics of house heating and energy flow in houses. Heat loss and heat transfer from radiators.
- Minimum requirements for indoor temperature levels, related to quality of living.
- District heating connections to houses, obligatory equipment, heat exchangers.
- Mathematical representation of district heating systems, steady and unsteady operation.
- Base load for district heating suppliers, its determination based on weather data.
- Swimming pools.
- Greenhouses and heating of soil.
- Snow melting and the use of heat in industry.
- Fish farming.
- Heat pumps.
Face-to-face learningPrerequisitesNot taught this semesterVÉL601MFish Processing Technology 2Elective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionThe main goal of the course is to train students to use their knowledge from various fields in mechanical engineering to organize and design fish processing plants and companies. Design requirements and design of production processes for fresh fish, frozen fish, dried fish, fish meal and canning plants. Production management, productivity estimates, quality control, wage structure, etc. for such companies. Heat and mass balances, steady and time dependent heat transfer, utilization of Heisler- and Mollier charts.
Exercises: Fish processing company or certain processes are analyzed and/or redesigned.
Face-to-face learningPrerequisitesNot taught this semesterEÐL624MIntroduction to NanotechnologyElective course8Free elective course within the programme8 ECTS, creditsCourse DescriptionNanostructures and Nanomaterials, Nanoparticles, Nanowires, Thin films, thin film growth, growth modes, transport properties. Characterization of nanomaterials, Crystallography,Particle Size Determination, Surface Structure, Scanning Tunneling Microscope, Atomic Force Microscope, X-ray diffraction (XRD), X-ray reflectometry (XRR), Scanning Electron Microscpe (SEM), and Transmission Electron Microscopy (TEM). Scaling of transistors, MOSFET, and finFET. Carbon Nanoscructures, Graphene and Carbon nanotubes. Lithography. Nanostructred Ferromagnetism. Nano-optics, Plasmonics, metamaterials, cloaking and invinsibility. Molecular Electronics.
Face-to-face learningPrerequisitesNot taught this semesterIÐN202MInnovation, Product Development, MarketingElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionAn insight into the structure of innovation, product development and marketing and how to use this methodology as a tool of management in industrial companies. Theory and practical methods of innovation, product development and marketing. Training in project management and how to run integrated projects covering those three areas by solving realistic problems.
Face-to-face learningPrerequisitesIÐN216FField Course in Innovation and Entrepreneurship (II)Elective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionThe course is a continuation of the course "Field Course in Innovation and Entrepreneurship (I)". This part of the course consists of detailed development of the business model related to a particular business opportunity. This work takes place in groups, where cross-disciplinary collaboration, between individuals with a background in business and individuals with a background in a particular technical or professional field related to the relevant opportunity, is emphasized. Projects can originate in an independent business idea or in collaboration with companies that partner with the course. In both cases, the emphasis will be on product or service develepment, built on technical or professional expertise, where the business case of the opportunity and its verification is in the foreground.
Face-to-face learningPrerequisitesCourse taught second half of the semesterIÐN222FField Course in Innovation and Entrepreneurship (I)Elective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionThe course is taught in two parts, and the expectation is that students register for both parts. The course will cover the practical issues related to innovation and entrepreneurship. It covers the emergence of a business idea and the initial evaluation of the business opportunity, and the development and testing of a business model. This part of the course consists of lectures and case discussions that deal with various aspects of innovation and entrepreneurship: Analysis of business opportunities, evaluation of market size and unit contribution, the management of organizational units that are involved in innovation, financing, and other issues. Students will also tackle projects where they apply the methods taught in the class to isolated tasks in product and business development in both new and existing firms.
Face-to-face learningPrerequisitesCourse taught first half of the semesterNot taught this semesterLVF601MIntroduction to Systems BiologyElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionSystems biology is an interdisciplinary field that studies the biological phenomena that emerge from multiple interacting biological elements. Understanding how biological systems change across time is a particular focus of systems biology. In this course, we will prioritize aspects of systems biology relevant to human health and disease.
This course provides an introduction to 1) basic principles in modelling molecular networks, both gene regulatory and metabolic networks; 2) cellular phenomena that support homeostasis like tissue morphogenesis and microbiome resilience, and 3) analysis of molecular patterns found in genomics data at population scale relevant to human disease such as patient classification and biomarker discovery. In this manner, the course covers the three major scales in systems biology: molecules, cells and organisms.
The course activities include reading and interpreting scientific papers, implementation of computational algorithms, working on a research project and presentation of scientific results.
Lectures will comprise of both (1) presentations on foundational concepts and (2) hands-on sessions using Python as the programming language. The course will be taught in English.
Face-to-face learningPrerequisitesRAF614MRobotics and Computer VisionElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionMathematical foundations of coordinate systems and transformations. Kinematics, direct and reverse solutions. Analysis and control of movements. Trajectories in three-dimensional space and interpolation between programmed trajectory points. Use of computer vision, sensors and end-effectors in robotics. Control and programming of robots. Excercises and simulations.
Face-to-face learningPrerequisitesRAF615MScience and innovation in medical technologyElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionThis course provides an introduction to the diverse applications of electrical and computer engineering in medicine and medical technology. Students will explore cutting-edge developments in the field through guest lecturers from industry professionals in Iceland who apply engineering methods to solve critical medical challenges. Key topics include signal and image processing in medicine and genetics, signal processing and sensors in relation to sleep and the central nervous system, prosthetics, artificial intelligence, and more. Finally, students will have the opportunity to design their own research projects focusing on applying engineering solutions to address medical challenges. Through this work, students will be introduced to writing research proposals and grant applications, with relevance to both industry and academic settings.
Face-to-face learningPrerequisitesCourse DescriptionIn this course, we study the AI lifecycle, i.e. the productionisation of AI methods.
We explore the following parts of the lifecycle:
- Data collection and preparation
- Feature engineering
- Model training
- Model evaluation
- Model deployment
- Model serving
- Model monitoring
- Model maintenance
Three large projects will be handed out during the semester where students compete to solve AI problems.Face-to-face learningPrerequisitesVÉL441LFinal projectMandatory (required) course0A mandatory (required) course for the programme0 ECTS, creditsCourse Description- The topic of the Master's thesis must be chosen under the guidance of the supervisor and the Faculty Coordinator of the student. The thesis represents 30 or 60 credits. All Master's student have been assigned to a Faculty Coordinator from the beginning of their studies, who advises the student regarding the organization of the program. If a student does not have a supervisor for the final project, he / she must turn to the Faculty Coordinator for assistance.
- The choice of topic is primarily the responsibility of the student in collaboration with his or her project supervisor. The topic of the project should fall within the student's area of study, i.e. course of study and chosen specialisation.
- The master’s student writes a thesis according to the School’s template and defends it in a master’s defense.
- Final project exam is divided into two parts: Oral examination and open lecture
- Present at the oral exam is the student, supervisor, examiner and members of the Master's committee. The student presents a brief introduction on his / her project. It is important that the objectives and research question(s) are clearly stated, and that main findings and lessons to be drawn from the project are discussed.
- The student delivers a thesis and a project poster.
- According to the rules of the Master's program, all students who intend to graduate from the School of Engineering and Natural Sciences need to give a public lecture on their final project.
- All students graduating from the University of Iceland shall submit an electronic copy of their final Master's thesis to Skemman.is. Skemman is the digital repository for all Icelandic universities and is maintained by the National and University Library.
- According to regulations of University of Iceland all MS thesis should have open access after they have been submitted to Skemman.
Learning Outcomes:
Upon completion of an MS thesis, the student should be able to:
- Formulate engineering design project / research questions
- Use an appropriate theoretical framework to shed light on his / her topic
- Analyze and solve engineering tasks in a specialized field.
- Perform a literature search and a thorough review of the literature.
- Demonstrate initiative and independent creative thinking.
- Use economic methodology to answer a specific research question
- Competently discuss the current knowledge within the field and contribute to it with own research
- Work with results, analyze uncertainties and limitations and interpret results.
- Assess the scope of a research project and plan the work accordingly
- Effectively display results and provide logical reasoning and relate results to the state of knowledge.
Self-studyPrerequisitesPart of the total project/thesis creditsVÉL049FSelected Topics in Mechanical EngineeringElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionLectures on and study of selected topics in current research and recent development in the field of Mechanical engineering. Topics may vary.
Students contact the teacher and the chair of department regarding registration for the course.
Self-studyPrerequisitesVÉL215FComputational Fluid DynamicsElective course7,5Free elective course within the programme7,5 ECTS, creditsCourse DescriptionThe main purpose is to develop methods of predicting numerical solutions in fluid mechanics and heat transfer. Especially of predicting boundary layer phenomena and modelling of turbulence transport properties. Both finite volume and finite difference methods are demonstrated. Solution of non-linear equations and stability criterium. Emphasis is laid on solution of practical problems.
The course is taught every other year on odd numbered years.
Face-to-face learningThe course is taught if the specified conditions are metPrerequisitesREI204MHigh Performance ComputingElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionDesign of parallel computers and parallel programming models. Shared memory architecture. Message passing and distributed memory architecture. Parallel programming of computer clusters using MPI and multicore programming using OpenMP. Parallel algorithms for sorting, searching, linear algebra, and various graph problems.
Course topics will be very similar like HPC in Fall 2019:
http://www.morrisriedel.de/hpc-course-fall-2019
Positioning in the Field of High-Performance Computing (HPC)
- Consists of techniques for programming & using large-scale HPC Systems
- Approach: Get a broad understanding of what HPC is and what can be done
- Goal: Train general HPC techniques and systems and selected details of domain-specific applications
Course Motivation
Parallel processing and distributed computing
- Matured over the past three decades
- Both emerged as a well-developed field in computer science
- Still a lot of innovation, e.g. from hardware/software
‘Scientific computing‘ with Maple, Matlab, etc.
- Performed on small (‘serial‘) computing machines like Desktop PCs or Laptops
- An increasing number of cores enables ‘better scientific computing‘ today
- Good for small & fewer complex applications, quickly reach memory limits
‘Advanced scientific computing‘
- Used with computational simulations and large-scale machine & deep learning
- Performed on large parallel computers; often scientific domain-specific approaches
- Use orders of magnitude multi-core chips & large memory & specific many-core chips
- Enables ‘simulations of reality‘ - often based on known physical laws and numerical methods
Face-to-face learningPrerequisitesVÉL072MSelected Topics in Mechanical EngineeringElective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionLectures on and study of selected topics in current research and recent development in the field of Mechanical engineering. Topics may vary.
Students contact the teacher and the chair of department regarding registration for the course.
Self-studyPrerequisites- Year unspecified
MAT508MFish Processing Technology 1Elective course6Free elective course within the programme6 ECTS, creditsCourse DescriptionThe role of the fish industry in the Icelandic economy. Fish as raw material, its composition, physical and chemical properties. Fish stocks, fishing gear, selectivity. Storage methods on board and after landing. Processing methods, production process and processing equipment for cooling, superchilling, freezing, salting, drying, canning and shell process. Energy and mass balance for each step in the process and the whole process.
Face-to-face learningPrerequisites