Jane Synnergren
Professor of BioInformatics
School of Bioscience
Introduction to AI in Bioinformatics A1N
Course, Master's (2nd cycle),
7.5 credits,
BI763A
Application
Choose a course instance to see course syllabus and admission requirements.
Spring 2026, Location: Skövde, Pace of study: 50%
Application
Application is done after nomination.
Course syllabus, with reading list
When? Where? How?
Study period: 19 January 2026 - 29 March 2026
Location: Skövde, Campus, Daytime
Pace of study: 50%
Admission Requirements
Entry requirements higher education not set. 150 approved higher education credits, of which a total of at least 90 credits in one or more of the subjects biology, biomedicine or computer science (or the equivalent). The subject biology refers here to programs with a bioinformatics, systems biology, molecular biology or microbiology focus. Of these, at least 15 credits must be at level G2E (or the equivalent). Furthermore, courses of at least 7.5 credits in the subject bioinformatics, 7.5 credits in cell biology and 15 credits in programming (or equivalent knowledge) are required. A further requirement is proof of skills in English equivalent of studies at upper secondary level in Sweden, known as the Swedish course English 6 or English level 2. This is normally demonstrated by means of an internationally recognized language test, e.g. IELTS or TOEFL or the equivalent.
Selection
Guaranteed admission.
Language
The courses are conducted in English.
What happens when AI meets biology? In this course, you will delve into the possibilities of machine learning – from classical algorithms to deep learning – and how they can be used to analyze biological data. Using modern programming tools, you will learn to build, understand, and evaluate AI models that make a difference in biological research.
Are you curious about how artificial intelligence can help solve some of biology’s biggest challenges? In this course, you’ll learn how machine learning – with a special focus on deep learning – can be applied to analyze complex biological data, such as gene expression or image analysis. You’ll gain a deeper understanding of how different AI methods work, and when it’s most appropriate to use supervised or unsupervised learning. Through hands-on exercises, you’ll use modern programming platforms and libraries to build, train, and evaluate AI models. You'll work with real biological problems and learn how to properly prepare and structure data to get meaningful results. Version control, model evaluation, and method selection are all integral parts of the course. We also discuss the opportunities and limitations of using AI in biological research. Whether you want to pursue a career in research, work in the biotech industry, or simply understand how AI works in practice, this course provides a solid foundation. You’ll gain both theoretical knowledge and practical skills – a combination that is highly relevant in today’s and tomorrow’s job market. In short: this is a course for those who want to help shape the future of biology through AI.
