GS-VMAS PhD Courses

Course code: PVG0044

Credits: 4

Course dates: Join continuously

The course has a dual format. One part is to actively participate in two workshops (4 hours per workshop) dealing with both oral and poster presentation techniques and the practical construction of a poster. Another part is to actively participate in >20 seminars given in the course seminar series. The seminar series will have weekly seminars during the spring and autumn semesters.

Course syllabus

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Course code: P000137

Credits: 7.5

Course dates: 1 Sept - 3 Oct 2025

Explore the fundamentals of supervised learning algorithms, especially for agricultural and natural science datasets. Implement and interpret Machine Learning models, starting with statistical models. Learn how to choose and divide your datasets (testing and training). Select appropriate variables. Then brush up on your Python and move on to Deep Learning. Learn about the XOR problem, back-propagation, and regularization. Learn what convolution is, and use it to classify images. Learn about U-Net and ResNet, and their applications in machine vision.

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Course code: P000043 

Credits: 7.5

Course dates: 1 Sept - 2 Nov 2025 (50% speed, can be read in parallel with P000042)

The course intends to provide advanced knowledge of theory and methods for studies of eukaryotic genomes, including their organization and evolution. There is a focus on animal genomics, but theoretical and methodological aspects of the course are applicable on many different organisms.

Course syllabus

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Course code: P000042

Credits: 7.5

Course dates: 1 Sept - 2 Nov 2025 (50% speed, can be read in parallel with P000043)

This course intends to provide advanced knowledge of theory and methods for analysis of eukaryotic genomes, including their organization and evolution. There is a focus on animal genomics, but theoretical and methodological aspects of the course are applicable on many different organisms.

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Course code: P000012

Credits: 3

Course dates: 8 - 12 Sept 2025

The aim of this course is to learn method approaches to analyse movement data on animals in natural, semi-natural or indoor environments. The focus will be on using existing R packages for movement analysis evaluating, how animals are affected by their environments and what drives their behavioural decisions. The course will also deal with animal social interactions both from the individual perspective, ie how agonistic behaviour in a group affect individual performance, and from a group interaction perspective i.e. how group members are dependent on each other in their behavioural decisions. We will exemplify these behaviours and decisions to three different systems 1) free-ranging reindeer use of natural pastures, 2) horses/cows in semi-natural pastures, and 3) cows in an indoor environment. GPS-data, local locational data systems (e.g., ultra-wideband) will be used during the course.

Course syllabus

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Course code: P000007

Credits: 3

Course dates: 23 Sept - 02 Dec 2025 (meetings approx. once a week)

The course focuses on primary production of animal source food and how the complete system up to farm-gate affects the environment. Different topics will be covered by experts in the fields in eight lectures followed by discussions. Relevant literature will be provided for the students to read before each lecture. The course meetings will be spread over one complete semester. The examination consists of a written summary submitted to the course leader team where the students relate their own research project to the different themes and an oral presentation given in a group session with other students. Other students will contribute with asking questions and give their perspectives on each presentation. Online participation will be facilitated.

Course syllabus

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Course code: P000007

Credits: 2

Course dates: 13 - 17 Oct 2025

The course is built around the feed cycle, i. e. the production of feed crops; their storage, handling and feeding; and the recirculation of animal wastes to new feed crops. The contribution from each step as well as the aggregated environmental impact for entire production systems are highlighted. Animal-specific properties of dairy and beef cattle, horses, pigs and poultry that are linked to environmental issues are covered. Greenhouse gases and losses of P and reactive N are the emphasized issues. Self-supplying systems serve as a model, but the actual situation with global trade of feeds, fertilizers and other inputs is also covered. The course time is equally divided between lectures and group work.

Course syllabus

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Course code: PVG0047

Credits: 3

Course dates: 13 - 24 October 2025

We will give a theoretical overview of what the One Health concept is, and why a one health approach is necessary to provide efficient solutions to the global challenges of our time. We will describe the benefits (and drawbacks) of transdisciplinary research and how transdisciplinarity is used in this context. Theoretical lectures will be mixed with presentations of successful one health cases. Methods commonly used within the one health concept will be presented, e.g. including epidemiology, bioinformatics, and qualitative research methods.

Participants will present their own research project to the group and after the first course week they will give a second presentation describing how the project could be developed using one health concept and methodology. The second week, students will be divided into groups and given the task to come up with a one health research project, and a skeleton for a research application.

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Course code: PVS0141

Credits: 10

Course dates: 3 Nov 2025 - 19 Jan 2026

The course aims to give the student a solid foundation in the basic methods of bioinformatics. It will cover the theories, algorithms and practical applications of computer-based methods for analysis of DNA sequences and protein structures. The topics to be covered include: - biological databases - homology analysis - sequence analysis (with emphasis on the package EMBOSS and UGENE) - statistics and R introduction - web-based tools for analysis - the Unix operating system - phylogeny - comparative genomics - functional genomics - Next Generation Sequencing (NGS).

Course syllabus

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