AI & Data Science Fellowship Program

The joint AI & Data Science Fellowship Program between the University of Tübingen and Boehringer Ingelheim is setting the next milestone in the development of new perspectives in AI and data science for healthcare.

This partnership offers postdoctoral talents the opportunity to link innovative academic research with application relevance for human and animal healthcare – while hosted at a highly renowned university and working in close cooperation with a leading pharmaceutical. Under supervision of University of Tübingen's Principal Investigators (PIs) and mentorship by Boehringer Ingelheim's scientists, postdoc fellows will benefit from our excellent research infrastructure to deliver outstanding results. Nominated fellows also participate in our exchange program between academia and industry by attending e.g., campus and company visits, networking events, and conferences.

Read more about our Fellows and their projects below.

Research Interests

• reinforcement learning
• decision-making
• gamified cognitive tasks
• computational psychiatry
• data science

Project: Using Gamified Assessments and Machine Learning to Create and Track Cognitive Profiles of Depression

Learning about our environment, and making decisions to achieve desired outcomes are critical for human functioning, and can be disrupted by mental health problems. This project will use machine learning on large-sample data from a gamified assessment system to develop cognitive learning and decision-making profiles to predict symptoms and shifts of depression. Such rapid, cost-efficient and easy-to-implement cognitive markers promise earlier detection of mental health issues and faster treatment.

Email Floor

Research Interests

• probabilistic deep modelling
• modelling of dynamical systems and time series 
• medical applications
• model discovery

Project: Simulation-based model discovery for time-series models in quantitative systems pharmacology

This interdisciplinary project aims to develop probabilistic machine learning tools for automatically inferring mechanistic dynamical system models from time series observations. The machine learning tools will be adapted to study disease mechanisms that underly Chronic Kidney Disease by extending existing limited models.

Email Daniel

Research Interests

• Information Retrieval

• Natural Language Processing

• Generative AI

Project: Language Model-guided Cohort Discovery

Medical and pharmaceutical innovation relies on clinical trials as a crucial quality control step. Most trials are delayed or altogether fail due to unrealistic trials design. This project aims to fundamentally change the way in which clinical trials are designed and recruited for, by using foundation models to get better insights into eligible cohorts, recruitment bottlenecks and efficiently discover lessons learned during thousands of historic clinical trials. It lives at the intersection of basic scientific innovation of NLP models and applied impact on the life sciences.

Shrestha’s Website

Email Shrestha

Research Interests

• theoretical foundations of machine learning
• analog-to-digital conversion algorithms
• applied mathematics

Project: Anomaly detection for histopathology to optimize drug discovery

We conduct research on anomaly detection for histopathology. The goal is to find any deviation from healthy tissue, such as pathologies, based on image data. The approach should learn the appearance of healthy tissue, be able to take advantage of certain known pathologies, but in particular detect completely new changes. Furthermore, it should be able to improve continuously getting different kind of human feedback from pathologists. Fast detection of anomalous tissue is important for early detection of toxic drug candidates.

Olga's Google Scholar Profile

Contact Olga

Research Interests

• Medical Informatics
• Quantitative Morphology
• Genomics and Transcriptomics
• Artificial Intelligence for Healthcare
• Representation Learning

Project: Learning joint representations of EHR and omics data

The project will work on large biobank data combining EHR and genetic information to learn the best joint representation that improves downstream analyses. Those downstream analyses include disease subtyping, disease risk prediction, and patient stratification.

Contact Tobias

Research Interests

• machine learning
• algorithm and method development
• computational genomics and large-scale genetic data analysis

Project: Leveraging Large-Scale Genealogies for Genome Wide Association Studies

Genome-wide association studies (GWAS) aim to identify loci in the genome that are causally linked to a disease. However, in large-scale genetic datasets, individuals are related through shared ancestry, which can be represented using ancestral recombination graphs. This project explores how such genealogical information can be combined with machine learning techniques to improve the detection of causal variants – ultimately enabling more effective, targeted treatments.

Email Lukas

Research Interests

• Cutting-edge Science and innovative Technologies
• Single-cell and bulk Transcriptomics
• Machine Learning to improve biomedical research
• Human Health & Pharma

Project: Transcriptomics machine learning models

This project will build hybrid transcriptomics machine learning models for drug discovery. Unsupervised learning will be used to build cell-type specific and integrated bulk tissue modules, then supervised learning will be applied to connect those modules to a holistic hybrid machine learning model for transcriptomics.

Contact Maik

Research Interests

• machine learning for infectious diseases

• enhancing biocatalysis with machine learning

• adaptation and development of explainable machine learning methods

• curation of high-quality datasets for ML training

Project: Predictive Biocatalyst Selection for Metabolite Synthesis

Identifying suitable catalysts for chemical transformations is crucial to developing more efficient and environmentally friendly processes. Advances in machine learning and database curation have the potential to accelerate this process in the pharmaceutical industry. This project explores task-specific machine learning algorithms to identify and generate enzymes from the vast and diverse world of bacterial secondary metabolism. This approach aims to streamline the selection process for complex biocatalytic reactions and enhance the development of greener, more efficient industrial processes.

Email Jacqueline

Research Interests

• computational neuroscience
• latent representation learning
• normative modeling across modalities
• deep generative models for brain imaging
• AI applications in mental health

Project: Data-Oriented Synergistic Embedding (DOSE) for Precision Psychiatry

This project develops a multi-modal normative modeling framework based on latent representations learned from structural and functional brain imaging data. We aim to capture individual variation across developmental and clinical dimensions using deep generative models. By aligning latent spaces across modalities and linking them to behavioral and cognitive scores, we seek to enable personalized characterizations of brain function and support stratified approaches in mental health research.

Mariam's Google Scholar Profile

Email Mariam

Research Interests

• biology with a focus on cell culture, molecular biology and transcriptional regulation
• molecular medicine in diabetes prevention
• data science and bioinformatics

Project: Multi-modal deep learning for biomarker discovery in mass spectrometry imaging data

Tissue microenvironment and the spatial organization of cells in the tissue are strongly associated with physiological and pathological processes. Mass spectrometry imaging (MSI) data contains distribution patterns of numerous molecules and their molecular masses. This research project will develop novel machine learning methods to associate MSI data with preclinical phenotypes, with the goal to discover novel molecular markers (as toxicity or efficacy markers or to highlight unexpected metabolic reactions).

Christoph's LinkedIn Profile