Educators & Organizers:
Oliver Kohlbacher (CIBI), Philipp Thiel, Manfred Claassen, Carsten Eickhoff, Kerstin Ritter

Date:
02-03-2026 to 06-03-2026

Location:

Conference Center at Heiligkreuztal Monastery
(Tagungshaus Kloster Heiligkreuztal)
Am Münster 7
88499 Altheim-Heiligkreuztal
Germany

https://www.kloster-heiligkreuztal.de/
https://maps.app.goo.gl/9gKrCXXoj1aNowrw9

Contents:
Increasingly large machine learning models are transforming how research is done in the life sciences. Such models enable addressing research questions with complex data modalities, and further to jointly consider multiple such data modalities to this end. While such approaches show impressive capabilities to establish non-trivial input-output relationships, interpretation of the underlying models remains a challenge.

Our spring school aims at bridging this gap by covering interpretable machine learning approaches to study various data modalities encountered and integrated in translational research projects. Specifically, we plan to consider natural language-, radiological- and molecular imaging data. The spring school will comprise input lectures and integrated project work that will be supervised by invited lecturers and their teams.

Specifically, we will cover lectures on interpretable models of single-cell biology, radiological data and natural language. These lectures will introduce basic and advanced methodological concepts and their application in translational projects. The spring school participants will apply these concepts in hands-on workshops on multimodal datasets covering the data modalities introduced by the lecturers with the goal to identify potentially novel intermodal patterns of translational relevance.

Agenda:

Arrival - March 2

from 2.00 pm

arrival at venue

6.00 - 7.00 pm

dinner

Day 1 - March 3

09.00 - 10.00 am

Introduction round/activity participants & trainers

10.00 - 12.00 am

Input lecture: Interpretable Machine Learning Models for Single-Cell Biology (Claassen)

12.00 - 01.00 pm

Lunch break

01.00 - 01.30 pm

Introduction to spring school data set(s) & definition of teamwork goals

01.30 - 02.00 pm

Definition teams

02.00 - 06.00 pm

Teamwork interpretable machine learning models for single cell biology

06.00 - 07.00 pm

Dinner

07.00 -

Evening activity

Day 2 - March 4

09.00 - 12.00 am

Input lecture: Interpretable Machine Learning Models for Medical Imaging Data (Ritter)

12.00 - 01.00 pm

Lunch break

01.00 - 03.00 pm

Team activity (e.g. hiking)

03.00 - 06.00 pm

Teamwork interpretable machine learning models for radiology

06.00 - 07.00 pm

Dinner

07.00 -

Evening activity

Day 3 - March 5

09.00 - 12.00 am

Input lecture: Language Modeling and Interpretation (Eickhoff)

12.00 - 01.00 pm

Lunch break

01.00 - 06.00 pm

Teamwork large language models for interpretation

06.00 - 07.00 pm

Dinner

07.00 -

Evening activity

Day 4 - March 6

09.00 - 12.00 am

Consolidation results and preparation of final presentation

12.00 - 01.00 pm

Lunch break

01.00 - 05.00 pm

Concluding symposium and discussion

05.00 - 05.30 pm

Wrap-up and departure

Learning goals:
Participants will gain hands-on experience in interpretable machine learning for multimodal biomedical data, developing the skills to collaboratively design and implement (publication-ready) bioinformatic analysesthat drive insight and impact in translational research.

Prerequisites:
You are a passionate PhD student or postdoctoral researcher eager to work at the intersection of cutting-edge data science and biomedical discovery?

We welcome applicants from two complementary backgrounds:

Bioinformatics, machine learning, or data science, with a keen interest and some hands-on experience in analyzing biological or medical data.
Experimental biologyor translational medicine, with a strong track record of performing your own data analyses using bioinformatics or machine learning methods.
If you’re excited about bridging disciplines and unlocking insights from complex biomedical data, and you have solid programming skills in Python, we’d love to have you on board.

You must bring a modern laptop with WLAN and Python development capabilities

Keywords:
Interpretable AI, ML in life sciences, multimodal data, translational research, AI in biomedical research, data integration

Tools:
Python