Courses on PhD level

Course start: VT 2021
Course responsible: Jörgen Hansson

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 plan

Course start: HT 2021
Course responsible: Jörgen Hansson

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.

Course plan

Course start: VT 2021
Course responsible: Oskar MacGregor

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 plan

Course start: HT 2021
Course responsible: Masood Fathi

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.

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Course plan

Planned course start: HT 2023
Course responsible: Sunith Bandaru

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.

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Course plan

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Course responsible: Masood Fathi

Within the framework of the course, current research questions and research results within the subject are analyzed and discussed. The PhD student will receive training in presenting research as well as providing constructive feedback on the research work of others.

The course "Scientific workshop in informatics I" is a prerequisite for attending this course.

Course plan

Planned course start: VT 2021 (prel. course start no earlier than week 8)
Course responsible: Susanne Durst

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

Course plan (only available in Swedish)

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Course plan (only available in Swedish)

Planned course start: VT 2022
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Course plan

Planned course start: HT 2021
Course responsible: Jörgen Hansson

The pressure to receive external research funding has increased strongly during recent years and funding agencies make higher and higher demands. It is therefore important to understand both implicit and explicit demands. The course addresses important issues needed to establish externally funded research projects.

After completed course the PhD student should be able to:

• understand the composition of the Swedish system for research funding
• understand how a research idea can be developed into a research project, considering e.g. collaboration partners, funding and communication
• identify relevant calls
• understand how to design a grant application of high quality that fulfills the funding agency’s demands and requirements
• understand the steps following the funding agency’s decision until the start of a project.

Course plan

Planned course start: HT 2022
Course responsible: Masood Fathi

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.

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Course responsible: Beatrice Alenljung

Course plan

Planned course start: November 6, 2020
Course responsible: Ulf Johansson (Jönköping University)

This course focuses on algorithms, techniques and methods for data analysis within the retailing domain. Specifically, the course covers both state-of-the-art research findings and established best practices of data analytics in retailing. An important part of the course is to study a number of important applications in the retailing domain where data analysis has been used successfully.

Course plan

Planned course start: HT 2021
Course responsible: Lissa Holloway-Attaway

Course plan