Courses at third cycle

Course start: spring semester 2024 (is given every year)
Course coordinator: Jörgen Hansson

Course syllabus IT0918F (pdf)

The course is aimed at PhD students who must discuss, plan, execute, report and critically review research results from various studies and from scientific literature. This course goes through basic scientific concepts and methods. These are used as building blocks for designing and developing new methodologies for research projects within Informatics. These methodologies are needed to handle the often interdisciplinary problems regarding complex technical systems. Through lectures and practical assignments, the PhD students will acquire skills to review scientific literature, evaluate different approaches and current methods. The PhD student will learn to design and apply methodologies supporting systematic analysis and structured ways to report research projects. Moreover the PhD student will acquire skills to argue for some already established, combined methodological approaches and concepts, such as abduction, action research, design science, and combining case studies.

Course start: autumn semester 2023 (is given every year)
Course coordinator: Jörgen Hansson

Course syllabus IT0919F (pdf)

In this course, the PhD student will receive training in addressing scientific problems in informatics with well-founded arguments. Informatics is a broad field of science, which covers a number of subdisciplines. The course covers a wide range of key scientific theories within the subdisciplines that are included in the University's definition of information technology. The student also gets the opportunity to immerse himself in a certain theory based on his own interest. In addition, oral proficiency in English is trained by presenting and discussing relevant theories.

Note! It is strongly recommended, although not formally put forward as a course requirement, that you have completed a course in research methods (such as IT0918F – Research Methodology and Communication in Informatics) prior to this. Doing a research method course first is likely to yield significantly better results/value for you.

Course start: spring semester 2025 (is given every two years)
Course coordinator: Oskar MacGregor

Course syllabus IT0925F (pdf)

What are we allowed to, ethically speaking, do as part of our research? What kind of research is directly prohibited and what type is controlled and limited in any way? We can have a general idea of the answers to these questions, but for postgraduate students there may still be many question marks. The course examines practical and theoretical ethical frameworks for research, to begin answering these questions, thus clarifying what a researcher's ethical rights and responsibilities actually are.

Course start: autumn semester 2023 (is given every year)
Course coordinator: Peter Thorvald

Course syllabus IT0927F (pdf)

Within the course, current research questions and research findings are analyzed and discussed. The course also trains the PhD student to present research and to provide constructive feedback to other people's research work.

Course start: autumn semester 2023 (planned to be given continuously)
Course coordinator: Martin Gellerstedt

This course is offered for doctoral students on the third-cycle education in Health Science at the University of Skövde only. 

Course syllabus HV0901F (pdf)

This course will give you the opportunity to exchange knowledge and experiences in constructive dialogs, and to progress in scientific writing and peer-review work. This is done by seminars were you together with other participants review and discuss each other’s scientific texts. This also gives the opportunity to broaden the knowledge and understanding of the research area: Health in the digital society, both theoretically and methodologically.

Course start: autumn semester 2023 (planned to be given continuously)
Course coordinator: Martin Gellerstedt

Course syllabus HV0902F (pdf)

This course will give you the opportunity to exchange knowledge and experiences in constructive dialogs, and to progress in scientific writing and peer-review work. This is done by seminars were you together with other participants review and discuss each other’s scientific texts. This also gives the opportunity to broaden the knowledge and understanding of the research area: Health in the digital society, both theoretically and methodologically.

Planned course start: autumn semester 2023
Course coordinator: Ninitha Maivorsdotter

Course syllabus HV0903F (pdf)

This course focuses on how digitalization could lead to profound changes in how people’s health and well-being at the individual, group, and societal levels are achieved, promoted, maintained, or hindered. As a participant you will reflect on digitalization and its consequences for research questions within health science.

Planned course start: autumn semester 2024
Course coordinator: Viveca Larsson

Course syllabus HV0904F (pdf)

Planned course start: autumn semester 2025
Course coordinator: Sunith Bandaru

Course syllabus IT0917F (pdf)

The course will cover the following topics:

• optimality theory for single and multi-objective optimization
• classical optimization methodologies for solving single-objective optimization problems
• key concepts of multi-objective optimization
• standard optimization models
• metaheuristic algorithms for solving multiobjective optimization problems
• visualization techniques and multi-criteria decision making methods
• industrial applications of multi-objective optimization and decision analysis
• advanced topics like interactive multi-objective optimization for incorporating user preferences

Assignments will evaluate the doctoral student's understanding of theoretical aspects of optimization and related algorithms, while laboratory exercises will test the ability to apply them. The seminar assignment includes studies of relevant literature and helps to promote critical analysis of competing methods. The PhD student will also carry out a project in an area related to his/her subject area/research. The project will be reported individually; oral and written presentation.

Planned course start: spring semester 2026
Course coordinator: Susanne Durst

Course syllabus IT0932F (pdf, only available in Swedish)

This course will give the PhD student a deep introduction to qualitative research, design and implementation.

After completing the course the PhD student will

• know about different qualitative methods and methods that can be used to create empirical material
• know the differences between these methods and can argue for their methodological choices
• understand how to identify when a research topic requires qualitative research methods and be able to choose the most appropriate methods to answer the specific research question
• have learned what a qualitative article should contain and have a first practical experience of developing one

Planned course start: spring semester 2025
Course coordinator: Manfred Jeusfeld

Course syllabus IT0934F (pdf)

The course targets postgraduate students whose research area includes the need to create conceptual models of their domain. Method engineering is the activity of designing domain-specific information systems development methods that only include the constructs needed for the target domain, in particular when several modeling viewpoints need to be integrated. A viewpoint is for example the data viewpoint, which makes statements about the data concepts of the considered domain. At the end of the course, students are able to create their own modeling methods by defining their constructs and semantics.

Planned course start: autumn semester 2024
Course coordinator: Masood Fathi

Course syllabus IT0939F (pdf)

This course studies scientific strategies to support decision making through mathematical modeling. It seeks to design, improve, and operate complex systems through mathematical modeling and has various applications in business, engineering, health care, and industry. The emphasis will be on industrial optimization problems, but problems from other domains will also be discussed in the course.

In industrial optimization, heuristic methods are sometimes used in cases when analytic methods that always find an optimal solution could easily be applied. This course provides the student with a good background in analytic optimization methods to cope with a variety of industrial problems. The course provides knowledge about different forms of mathematical optimization models as well as exact solution approaches.

The course contains both a theoretical and a practical part. The theoretical part focuses on learning and developing different types of mathematical optimization models as well as learning and applying certain exact solution methods for solving industrial optimization problems. In the practical part, through a hands-on approach supported by computer software, the student will learn how to solve the mathematical optimization models using an appropriate method for each model type.

Planned course start: spring semester 2024 (is planned every two years)
Course coordinator: Beatrice Alenljung

Course syllabus IT0940F (pdf)

Planned course start: autumn semester 2024
Course coordinator: Lissa Holloway-Attaway

Course syllabus IT0942F (pdf)

In this course students will explore the relationship between technology development and design, information systems, and society in the context of intersectional and inclusive research perspectives. Particular focus will be on the ways bias and prejudice impact technological decision-making and may reinforce destructive and exclusionary power structures and forms of systemic oppression. Students will study and apply a variety of theoretical perspectives relevant to inclusive design practices and present and document research within their own study disciplines with a series of writing assignments.

Planned course start: spring semester 2026
Course coordinator: Sunith Bandaru

Course syllabus IT0945F (pdf)

Planned course start: spring semester 2024 (is planned every two years) 
Course coordinator: Joe Steinhauer

Course syllabus IT0946F (pdf)

This course addresses PhD students within informatics who wish to gain insights and general knowledge within data science. In today’s research it is often necessary to be able to analyze data. In order to gain information form data it is important to understand what techniques are suitable for one’s information needs and the given type of data, what pros, cons and potential risks the choice of algorithm entails, as well as how the result of the data analysis needs to be evaluated and presented.

This course will provide insights in several sub areas of data science, such as artificial intelligence, data mining, machine learning, predictive analysis and data visualization. If you are a PhD student within a research area where you will need to analyze data or need to understand other researchers’ data analyses, this course will provide you with the necessary entry-level knowledge. The course is on PhD level and will contain practical assignments but does not require programming skills. Therefore this course is interesting for PhD students with and without programming skills; for those who which to get started with data science projects as well as those who are looking for a general and theoretical understanding of methods and techniques within data science.

Planned course start: TBA
Planned course coordinator:

Course syllabus IT0947F (pdf)

Planned course start: spring semester 2025
Planned course coordinator: Maurice Lamb

Course syllbus IT0948F (pdf)

Healthcare, manufacturing, logistics, and transportation are only a few areas impacted by growing developments in human-robot interaction (HRI). This course is for anyone who thinks that their research may be impacted by or may impact the move of robots into interactions with humans.

The course does not require an ability (or desire) to program or build robots (though that is welcome too). This course will be a good introduction to how robots are being developed, designed, and evaluated to integrate into various areas of many fields and course discussions will be greatly enhanced by students from a mix of fields.

The course aims to deepen the students’ knowledge within the interdisciplinary field of HRI. Initially, the course introduces the emergence and roots of HRI, its interdisciplinary nature and its various applications. The course also focuses on the scope and character of current HRI research, addressing the state of the art in HRI, as well as a foreseeable direction toward which the field is developing.

It pays in-depth attention to the robot perspective, the human perspective and the interaction perspective of HRI. The course also addresses the challenges of being an interdisciplinary research field as well as ethical and societal issues that may arise in research and development of HRI.

Planned course start: autumn semester 2023
Planned course coordinator: Alexander Karlsson

Course syllabus IT0949F (pdf)

The course covers methods and tools for probabilistic modeling from a perspective which is present within the areas: artificial intelligence, machine learning and data science. For further information, see the course plan.