Machine Learning in Biology - Summer School

15-18 June 2026

Dr David Ferreira, UNIBE
Dr Stephan Peischl, UNIBE

Dr. Stephan Peischl, Interfaculty Bioinformatics Unit (IBU), University of Bern

Dr. David Miguel Ferreira Francisco, Interfaculty Bioinformatics Unit (IBU), University of Bern

Dr. Aparna Pandey, Interfaculty Bioinformatics Unit (IBU), University of Bern

Invited Speakers - Wednesday Symposium (17 June)

M. Robin Zbinden, Environmental Computational Science and Earth Observation Laboratory (ECEO), EPFL

Dr. Guillaume Witz, Microscopy Imaging Center (MIC) & Data Science Lab, University of Bern

Dr. Marco Baity-Jesi, Machine Learning & Complex Systems Group, Eawag (Swiss Federal Institute of Aquatic Science and Technology)

One last speaker to be announced

About the Course

This four-day summer school equips life scientists with the practical skills to apply machine learning to their own data. Mornings cover the methodological foundations - supervised and unsupervised learning, model evaluation, and the pitfalls specific to biological datasets. Afternoons are spent in hands-on coding sessions working with realistic biological problems.

The programme includes a one-day mini-symposium on Wednesday in which researchers present current applications of machine learning in biological and biomedical research, providing context for how the methods covered translate into ongoing science.

Who should attend

PhD students, postdoctoral researchers, and scientific staff in the life sciences who want to move beyond using machine learning as a black box.

Prior experience with R is helpful but not required; participants without programming background should expect to invest additional effort during the practical sessions.

What You Will Gain

A Working Vocabulary

The ability to read ML methods sections critically and discuss them with collaborators.

Hands-on Competence

You will train, validate, and interpret models on biological data during the course.

Judgement

Recognising when ML is the right tool, which method fits the question, and how to avoid the most common failure modes - data leakage, overfitting, batch effects.

A Network

Four days of close interaction with instructors, invited speakers, and peers from across Swiss life-science institutions.

See: https://ibusummerschool.netlify.app/#programme

UNIBE

Reimbursements for CUSO students: Train ticket, 2°class, half-fare from your institution to the place of the activity.

Reimbursement of your travel tickets can be asked online through your MyCUSO. See HERE for the procedure.

Accomodation: Students affiliated to the Universities of Lausanne and Geneva can be reimbursed up to 3 nights up to 100 CHF/night (incl. taxes + breakfast).

Please make sure to sign the presence sheet: without signature, no reimbursement will be possible!

https://ibusummerschool.netlify.app/#registration (deadline: June 8)

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Contact: Stefan Daniel · [email protected]