Course archive

Course archive

Here you can find information about past doctoral courses that have been given at BTH. The purpose is that you have access to past course syllabi should you wish for a course to be given again or adapted into an individual study course by your supervisors or Examiner.

Philosophy and Methodology of Applied Sciences (last given in 2021)

The aim for the doctoral student is to acquire knowledge and develop skills in the area of philosophy of
science and methodology of applied science. It aims to increase the students’ ability to formulate and
applied scientific principles within their own area of research.


PART 1: Theory/Seminars, 3 ECTS credits
− History of science: from experience facts to experimentalism;
− Modern theory of science: falsificationism, Kuhn’s paradigm, Lakato’s research programmes,
Feyerabend’s anarchistic theory of science, subjective Bayesians, and new experimentalism;
− Methodology of applied science;
− Legal and ethical aspects of publishing.
PART 2: Project/Workshops, 4.5 ECTS credits
− Approaching research problem – a research question and hypothesis;
− Validation and verification of research hypothesis;
− How to organise and write thesis and scientific paper;
− Tools for referencing and using templates.
− Presenting and disputing of research results;
− Reviewing of the research reports;
− Project and team work management.


Knowledge and Understanding
− fundamental concept and theory concerning modern paradigm in science, special in applied
− Academic and publishing culture.
Skills and Abilities
− Scientific writing;
− Research competence;
− Write, present and dispute scientific papers and reports.
Judgment and Approach
− Be able to analyse, review and oppose scientific papers and reports.

The examination consists of active compulsory participation in seminars and workshops, written
assignments submitted and presented in different ways.
Code Module Credit
Theory 3.0 ECTS
Project – individual part 2.0 ECTS
Project – group part 2.5 ECTS
Assessment of the course is the grade pass or fail (G/U).

Course literature and other teaching material

− A.F. Chalmers: What is this Thing Called Science? ISBN 0-87220-452-9.
− Course coordinator will provide suitable compendia and a list of supplementary literature before
the course starts

 Course coordinator/responsible

Wlodek Kulesza, Professor, BTH
9. Certificate
On the student’s request, course certificate is issued by BTH through the course coordinator.


Scientific Communication I and II, 2 hp (planned to be run in 2022)

Course descriptor

Course responsible: Krzysztof Wnuk


The main objective of the course is to teach the candidates to communicate scientific ideas and results both to academic audiences and to the public in general. The first part of the course (Scientific Communication I) will focus on the popular science format, while the second part (Scientific Communication II) will focus on the academic format. The course will give practical experience with presenting own ideas, as well as receiving feedback and critically reviewing others’ work.


The course will be organized around a set of seminars and workshops with candidate presentations.

  • Scientific Communication I – Popular science presentations
    • Seminar I:
      • Public understanding of science and dialog with society.
      • Formats and style. Storytelling. Popular science magazine articles and general press.
    • Seminar 2:
      • Presenting research ideas and results to public.
      • Formats and style. Elevator pitches, TED talks.
    • PhD Workshop
      • Candidates prepare a presentation of their ideas and results and give a presentation in an annual PhD workshop
  • Scientific Communication I – Academic presentations
    • Seminar 1:
      • Communicating scientific ideas and research to an academic audience. Format and style
    • Seminar 2
      • Scientific reviews. Providing and receiving constructive feedback.
    • PhD Workshop
    • Candidates prepare an extended of their ideas and results and give a presentation in an annual PhD workshop


Industry-academia co-production (next planned early 2023, contact

Corresponds to 3 higher education credits (högskolepoäng)

The aim is for the doctoral student to acquire advanced knowledge and skills in the industry-academia co-production. The course will discuss the following aspects:

  • Introduction to the process of co-production between industry and academia.
  • The main phases and steps involved in the process.
  • Role descriptions for the main actors involved in the process.
  • Knowledge dissemination and reporting.

 The objectives regarding knowledge and understanding include

  • In-depth knowledge of industry-academia co-production


Skills and Abilities

  • Ability to plan and execute research in co-production with industry partners.


Course literature and other teaching material

  • Gorschek T., Wnuk K. (2020) Third Generation Industrial Co-production in Software Engineering. In: Felderer M., Travassos G. (eds) Contemporary Empirical Methods in Software Engineering. Springer, Cham.
  • Gorschek, P. Garre, S. Larsson and C. Wohlin, “A Model for Technology Transfer in Practice,” in IEEE Software, vol. 23, no. 6, pp. 88-95, Nov.-Dec. 2006, doi: 10.1109/MS.2006.147.


  1. Course responsible

Krzysztof Wnuk, Department of Software Engineering,

Reading and Reviewing Research Papers (next planned after summer 2022, contact ) 

Third-cycle course

Corresponds to 5 higher education credits (högskolepoäng)

The objective of this course is to support doctoral students at BTH in the process of reading and reviewing academic work (conference, workshop, and journal publications). Moreover, the goal is to learn the students how to write good rejoinders of their own papers and work with reviewers and editors of academic conferences and journals.

The course includes five seminars where different aspects of reading, reviewing, and preparing a rejoinder are discussed and examples are provided. Moreover, the course contains two assignments where students show the ability to review a research paper and prepare a rejoinder to a research paper.

 Knowledge and understanding

  • Knowledge and understanding of the review process in academia
  • Knowledge and understanding of the rebuttal process in academia


Skills and Abilities

  • Ability to time-efficiently read research articles
  • Ability to time-efficiently review an assigned paper and prepare constructive comments
  • Ability to prepare a good rejoinder and address the reviewers’ comments on your own submissions
  • Ability to critically review your own research articles

Judgement and approach

  • Judge the effort and strategies needed for writing a good rejoinder
  • Judge the effort required when acting as a reviewer to a paper and the expected level of feedback


The teacher provides the necessary literature during the seminars

Course responsible

Krzysztof Wnuk, Department of Software Engineering,


Modern Methods of Statistical Analysis and Estimation, 7,5 hp

During the winter 2018/2019, The Department of Mathematics and Natural Sciences gave a statistics course for Ph.D. students in Mathematics and Engineering sciences. The course (7.5 hp) consists of the following areas:

  • Estimation in general
  • Regression analysis (including multiple and non-linear regression)
  • Analysis of variance (ANOVA) with application to planning of experiments
  • Non-parametric methods (Methods which can be used when the data are not Gaussian distributed)
  • Time series analysis including ARMA models (very applicable in signal processing and prediction in general, e.g. in economy)
  • Orientation about random processes (applicable in e.g. reliability and telecommunications)
  • Computer labs using the software SPSS and R


Textbook: Walpole, R.E. et al (2011 or later). Probability and statistics for engineers and scientists. Pearson Education.

Additional documents from the Department of Mathematics and Natural Sciences.


Prerequisite: a basic course in probability and statistics (at least 6hp).


Claes Jogréus, Department of Mathematics and Natural Sciences

Sustainability in Engineering Product Development, 20 hp (2016-2017)

A product development PhD Course suite of 20 hp in total, including four independent modules of 5 hp each.

(1) A Critical Review of the Product Development Process (PDP), 5 hec

(2) Sustainability in Engineering Product Development (SEPD), 5 hec

(3) Modeling, Simulation and Optimization (MSO), 5 hec

(4) Engineering Innovation and Management (EIM), 5 hec

This suite is recommended for PhD candidates in Engineering Product Development and the modules are given for the first time during 2016-2017

More information

Modeling Simulation and Optimization in the Engineering Product Development Process, 5 hp (spring 2018)

The course aims to provide a basic understanding for how modelling, simulation and optimization can be employed to support the product development process

Course website and more information

Computer Vision by learning, 7,5 hp (spring 2018)

The use of enormous computing processing power in combination with ease of accessing the network resources was a dream for twenty or even ten years ago. Thanks to these achievements we see today hundreds of applications pops up every day. Many of them implement computer vision learning algorithms on images, videos or 3D contents for object and pattern recognition, structural prediction, semantic content segmentation and tracking as some examples.

Course descriptor

Course in Research Funding, 2,0 credits (fall 2016)

The aim of the course is to strengthen PhD student’s capabilities to apply for external funding of research projects.
Content – Timeframe for proposals – The funding landscape – Funding search profiles – Systematic strategies for grant applications – Budgeting for projects – Writing and presenting research funding proposals.

Course descriptor

Research Ethics in Computing and Engineering, 3,0 credits (fall 2017)

The aim is for the doctoral student to acquire awareness, knowledge and capability to conduct research taking different ethical aspects into account. Content − Introduction to research ethics (seminar and discussions), − Focused study on a selected topic on research ethics (report and presentation), − Insights into different topics related to research ethics (seminar and discussions)

Course descriptor

1st International Summer School On Human Factors In Software Engineering


Period: Jun 2-5, 2020

The 1st International Summer School on Human Factors in Software Engineering will bring together internationally recognized scholars to discuss advanced topics on human factors in software engineering. The main goal of the summer school is to provide a contribution to Ph.D. students and academic and industrial researchers alike on latest findings in the field of human factors in software engineering. A particular focus will be set on research methods in that field.

Aim: The main aim of the summer school is to provide a contribution to Ph.D. students and academic and industrial researchers alike on latest findings in the field of human factors in software engineering. A particular focus will be set on research methods in that field.

Target group: The summer school is primarily intended for Ph.D. students working in the area of behavioral software engineering and/or studying the influence of human factors in software engineering.

Language: English.

Previous knowledge: Participants are expected to be working in the area, independent whether they are early or late in their studies. However, we also welcome students interested in the subject but not necessarily working in the field. The lectures will equip the students with the knowledge needed for the practical lab sessions, which will be also supervised.

Conditions: To be accepted to the summer school, those applying are expected to submit an ongoing or recently completed work for the doctoral symposium together with the registration.

Computer Vision by Learning, 7,5 hp

The course may be given as a 3,0 hp (assignments) + 4,5 hp (project) course 

Course responsible: Siamak Khatibi

In recent years, learning has become a dominant classification tool for a variety of domains. In computer vision, the tools have been used to promote object and pattern recognition, which have proven to be very successful. In this course we will study learning methods for various computer vision problems. In these methods either invariant features are detected and implemented in the learning process or simply original images/videos are used.

With this background, the course aims to provide students with insight into the fundamentals of advanced subjects in computer vision using learning methods.


Central items of the course are:

  • Overview of image segmentation and object detection
  • Modeling concept vice versa learning concept
  • Invariant features
  • Learning using invariant features
  • Deep learning for pattern recognition
  • Structural Prediction
  • Semantic image segmentation with deep learning
  • Tracking and event recognition with deep learning

Recommended prerequisites: Linear algebra and Programming

Course descriptor

Research Ethics, 3 hp

Course descriptor

Course responsible: Darja Smite


The main objective of the course is to raise awareness and knowledge about the various aspects related to research ethics and misconduct.

Content and tentative schedule

The course will be organized around a set of seminars and conclude with a workshop with participant presentations.

  • Seminar I [2020-10-01 9:00-11:00]: Introduction to research ethics.
  • Seminar 2 and Groupwork [2020-10-08 9:00-11:00]: Data handling.
  • Seminar 3 [2020-10-15 9:00-11:00]: Scientific authorship and scientific publishing.
  • Seminar 4 [2020-10-22 9:00-11:00]: Research misconduct.
  • Workshop [2020-11-19 9:00-12:00]: Students presentations (additional workshops might be booked depending on the number of course participants)

Note: The course is likely to be run on a distance.