Methods in Molecular Biology (LÍF118F)

Lectures: Theoretical basis of common molecular-biology techniques and their application in research. Course material provided by teachers. Laboratory practice in molecular biology techniques: Model organisms: E.coli, S. cerevisiae, C. reinhardtii, A. thaliana, C. elegans, D. melanogaster, M. musculus. Laboratory notebooks and standard operating procedures (SOP's), using online tools. Culture and storage of bacteria, yeasts and other eukaryotic organisms and cells. DNA and RNA isolation and quantification (Southern and Northern blotting, PCR, RT-PCR, qRT-PCR), restriction enzymes, DNA sequencing techniques and data analysis. Gene cloning and manipulation in bacteria yeasts and other eukaryotes. Protein expression and analysis. How to raise antibodies and use them. Western blotting, immunostaining, radioactive techniques. Microscopy in molecular biology. Methods used in recent research papers will be discussed. Essay and oral presentation discussing a selected technique. Problem based learning group assignment for graduate students: Experimental design and grant writing exercise with oral presentation of a research project.

Final project (LVF442L)

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.

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.

Research in molecular biology and biochemistry (LÍF114F)

This course is important for all graduate students in molecular biology and related fields. It is divided into two main parts: 1) presentation of a research article (journal club) and presentation of the research project (work in progress). For the research article students select a recent research article of interest and give a presentation on the aims and results. The goal is to train reading research articles in a critical way and present to others. The students have also as an assignment to bring up questions when other students present articles. The aim is to enhance critical approaches concerning scientific aims and methods used to reach the aims. In the project presentations students are expected to concentrate on the aims give a backgrounds and details of the methods used, results and the planned continuation. The students are expected to train in getting the message of the project in a clear way. The project presentation can lead to suggestion from other students that are useful for the approach.

This course is in English and has been on both spring and autumn term. The student can take this course four times giving max 8 ECTS.

Attendance is obligatory.

Human Genetics (LÍF513M)

Lectures: Mendelian genetics, organization of the human genome, structure of chromosomes, chromosomal changes and syndromes, gene mapping via association and whole genome sequencing methods, genetic analysis, genetic screening, genetics of simple and complex traits, genes and environment, cancer genetics, gene therapy, human and primate evolution, ethical issues concerning human genetics, informed consent and private information. Students are expected to have prior knowledge of the principles genetics.

Practical: Analyses of genetic data, study of chromosomal labelling, analyses of genetic associations and transcriptomes.

Microbiology II (LÍF533M)

The aim of this course is to introduce different applications of microorganisms and to help students develop independent research skills. In the first part of the course, students will visit a geothermal area and subsequently work on a research project where they isolate, identify and study bacterial strains.

The second part will introduce different fields of microbial biotechnology and how they have been shaped by recent progress in microbiology, molecular biology and biochemistry. State of the art will be covered regarding subjects such as microbial diversity as a resource of enzymes and biocompounds; bioprospecting, thermophiles, marine microbes and microalgae, biorefineries (emphasis on seaweed and lignocellulose), enzymes (emphasis on carbohydrate active enzymes), metabolic engineering (genetic engineering, omics), energy-biotechnology, cultivation and fermentation technology. The course will exemplify Icelandic biotechnology where applicable. Cultivation/production technology and yeast will be presented specifically in practical sessions in the brewing of beer.

The third part will cover environmental sampling, microbial communities and biofilms, microbes in aquatic and terrestrial environments, indoor air quality and the impact of molds. Also, water- and food-borne pathogens, risk assessment and surveillance, water treatment, microbial remediation, methane production and global warming. Students will visit waste management and water treatment plants and review and present selected research articles.

Additional teaching one Saturday in end of September or beginning of October.

Microbial biotechnology (LÍF534M)

This course introduces biotechnology-based applications of microbes and their enzymes. The first part provides fundamental microbiology such as the classification of microorganisms, their structure, metabolism, growth and functional characteristics, handling and identification. The content of the first part will be emphasized with practical sessions, discussions and written assignments and is the foundation for more specific topics.

The second part will introduce different fields of microbial biotechnology and how they have been shaped by recent progress in microbiology, molecular biology and biochemistry. State of the art will be covered regarding subjects such as microbial diversity as a resource of enzymes and biocompounds; bioprospecting, thermophiles, marine microbes and microalgae, biorefineries (emphasis on seaweed and lignocellulose), enzymes (emphasis on carbohydrate active enzymes), metabolic engineering (genetic engineering, omics), energy-biotechnology, cultivation and fermentation technology. The course will exemplify Icelandic biotechnology where applicable. The subject will be presented in lectures and students will be trained in reading original research papers on selected topics in the field; Cultivation/production technology and yeast will be presented specifically in practical sessions in the brewing of beer.

This course is partly taught in parallel with Microbiology II (LÍF533M) and intended for students that have neither completed Microbiology (LÍF201G) nor a similar course. Students must complete the first part of the course before participating in the latter. The number of participants might be restricted.

Additional teaching one saturday in end of September or beginning of October.

Environmental microbiology (LÍF535M)

The aim of this course is to introduce the importance of microorganisms in nature as well as in environmental applications. The first part provides fundamental microbiology such as the classification of microorganisms, their structure, metabolism, growth and functional characteristics, handling and identification. The content of the first part will be emphasized with practical sessions, discussions and written assignments and is the foundation for more specific topics.

The second part will cover environmental sampling, microbial communities and biofilms, microbes in aquatic and terrestrial environments, indoor air quality and the impact of molds. Also, water- and food-borne pathogens, risk assessment and surveillance, water treatment, microbial remediation, methane production and global warming. Students will visit waste management and water treatment plants and review and present selected research articles.

This course is partly taught in parallel with Microbiology II (LÍF533M) and is intended for students that have neither completed Microbiology (LÍF201G) nor a similar course.

Special topic in bioengineering (LVF101F)

Study of selected topics in current research and recent developments in bioengineering. Topics may vary.

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.

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.

Algorithms in Bioinformatics (TÖL504M)

This course will cover the algorithmic aspects of bioinformatic. We  will start with an introduction to genomics and algorithms for students new to the field. The course is divided into modules, each module covers a single problem in bioinformatics that will be motivated based on current research. The module will then cover algorithms that solve the problem and variations on the problem. The problems covered are motif finding, edit distance in strings, sequence alignment, clustering, sequencing and assembly and finally computational methods for high throughput sequencing (HTS).

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.

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.

Final project (LVF442L)

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.

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.

Separations (EVF401M)

The major equipment of chemical process plants is called "unit operastions" and consists mainly of three types. Firstly, there are reactors. Secondly separation equipment. Thirdly heat exchangers and boilers. This course covers the main examples of separation equipment used in industry. Heat exchangers will also be treated. The operating principles and modeling of every equipment type will be introduced. Students will simulate every equipment type in the process simulator Aspen. 

This course will also introduce students to process simulation software. For this purpose, it is highly recommended that every student have a Windows laptop or a Macintosh with virtual Windows installed.

Process Design (EVF601M)

A systematic introduction to the use of process simulators (like Aspen) to model, design and optimize chemical manufacturing processes. The selection, optimization and combination of reactors, separation equipment and heat exchangers. An introduction to the concepts and principles of project economics.

Reaction Design (EVF602M)

Design of chemical reactors for economical processes and waste minimization. Contacting patterns, kinetics and transport rate effects in single phase and catalytic systems. Another goal of the course is to introduce the fundamentals of mass transfer in chemical engineering such as the mass transfer theory and how to set up differential equations and solve them for such systems.

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.

Cell Biology II (LÍF614M)

The emphasis is on research articles. Resent research in various field with links to cell biology are included but can vary between years. For each lecture max three research articles are included.

Each student gives a seminar on one research article with details on methods and results. The students write a report (essay) on the article and discusses the results in a critical way.

Examples of topics included in the course: innate immunity, prions, the proteins pontin and reptin, polarized epithelium, development of trachea, data analyses and gene expression, autophagy, the origin of the nucleus.

Special topic in bioengineering (LVF201F)

Study of selected topics in current research and recent developments in bioengineering. Topics may vary.

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.

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

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.

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.

Algorithms in Bioinformatics (TÖL604M)

This course will cover the algorithmic aspects of bioinformatic. We  will start with an introduction to genomics and algorithms for students new to the field. The course is divided into modules, each module covers a single problem in bioinformatics that will be motivated based on current research. The module will then cover algorithms that solve the problem and variations on the problem. The problems covered are motif finding, edit distance in strings, sequence alignment, clustering, sequencing and assembly and finally computational methods for high throughput sequencing (HTS).