Uni-Tübingen

AI & Data Science Fellowship Program

Academia meets Industry –
University of Tübingen & Boehringer Ingelheim

The partnership 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. With this joint AI & Data Science Fellowship Program aimed for postdoctoral candidates, academic research will be linked with application related topics of the pharmaceutical industry.

CONTACT

Prof. Dr. Nico Pfeifer

Department of Computer Science
Sand 14
72076 Tübingen

datasciencespam prevention@inf.uni-tuebingen.de

About the Program

This Fellowship Program is designed to support postdoctoral talents who want to work on cutting edge research topics in the field of AI and Data Science – at one of the most renowned universities in Germany and in cooperation with one of the leading companies in the pharmaceutical industry.

The Fellowship Program offers up to five postdoc candidates per year the opportunity to work on innovative research questions with application relevance for healthcare – and you can be one of them! As nominated fellow you work for two years, with the option to extend for an additional year, on an exciting AI and Data Science research project, which was evaluated and confirmed through a Joint Steering Committee. Attractive benefits await you, such as a postdoctoral salary according to TV-L personnel rates, assistance with relocation and visa application. In addition, you participate in our exchange program between academia and industry by attending e.g., campus and company visits, networking events, and conferences.

You are hosted at the University of Tübingen and are supervised by highly recognized Principal Investigators (PIs) and mentored by scientists from Boehringer Ingelheim. At the University campus, you have access to a top research infrastructure, relevant data to conduct the research and the equipment that will enable you to deliver outstanding results.


OPEN RESEARCH TOPICS

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

In this interdisciplinary project you develop probabilistic machine learning tools for automatically inferring mechanistic dynamical systems models from time-series observations, then apply them to study e.g. disease mechanisms underlying Chronic Kidney Disease.

Application closed

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.

Application closed


RESEARCH FELLOWS

Dr. Christoph Hoffmann


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

 

Dr. Olga Graf


Research Interests
  • theoretical foundations of machine learning, including:
    • out-of-distribution detection
    • uncertainty quantification
    • Bayesian machine learning
    • physics-informed neural networks
  • 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

 

Maik Wolfram-Schauerte


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

 

Dr. Tobias Theska


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

 


Particularly in the combination of AI with relevant healthcare and pharmaceutical data, there is great potential to noticeably improve the wellbeing of many people.

Prof. Dr. Nico Pfeifer, Uni Tübingen

We are very excited to work with the fellows to extend our leadership in the development of next-gen AI and data science solutions.

Dr. Brigitte Fuhr, Boehringer Ingelheim


MORE INFO

FAQ

  1. What is the deadline for applications?
    You can find the deadline in the respective job advertisement.

     
  2. Which qualifications do I have to bring?
    First of all, you have to bring a PhD graduate in a relevant field. The detailed description of required qualifications and skills is listed in the job advertisement. Please check out the job board and consider the mentioned pre-requisites.

     
  3. Are PhD graduates outside of Germany eligible to apply?
    Yes. Our Fellowship Program is open to all PhD graduates at all universities both in Germany and other countries.

     
  4. Where will I be employed as a fellow?
    You will be employed at the University of Tübingen, whereas you are located and receive the salary.

     
  5. How long will I get funded as a fellow?
    The Fellowship Program for each candidate is planned for two years. In exceptional cases, the time can be extended for an additional year.

     
  6. How many fellows will be accepted?
    Up to five candidates per year can be funded through this AI & Data Science Fellowship Program.

     
  7. What documents do I have to provide with my application?
    Please consider the information, listed in the respective job advertisement for the research project.

     
  8. Who will evaluate my application?
    Your application will be reviewed by experienced employees/scientists of the University of Tübingen. At a later stage PIs from the University of Tübingen and Scientists from Boehringer Ingelheim are also involved in the selection process.
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  9. What percentage of my time is expected to be in the program?
    100 % of your time and effort is required to participate in the program.
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  10. Can I apply if I am not enrolled or employed at the University of Tübingen?
    Yes. All graduates with a PhD are invited to apply both from the University of Tübingen and other universities worldwide.
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  11. What benefits do I receive?
    ​​​​​​​We provide remuneration in accordance with the TV-L (collective agreement for public employees of the German federal states) as well as all corresponding benefits, e.g. extensive visa and onboarding assistance, 30 days/year of paid vacation, flexible working hours, discounted public transportation, etc.

Joint Steering Committee & Representatives

University of Tübingen

Methods in Medical Informatics
Prof. Dr. Nico Pfeifer
PfeiferLab
Twitter

Machine Learning in Science
Prof. Dr. Jakob Macke
MackeLab

Clinical Bioinformatics
Prof. Dr. Manfred Claassen
Clinical Bioinformatics


Boehringer Ingelheim

Head of Central Data Science
Dr. Brigitte Fuhr
LinkedIn

Global Head of Computational Biology and Digital Sciences
Jan Nygaard Jensen
LinkedIn

Head of Strategic Partnerships - Translational Medicine and Clinical Pharmacology
Alexander Staab
LinkedIn

Site Head Biberach and Member of the Boehringer Ingelheim Germany Executive Committee
Fridtjof Traulsen
LinkedIn


PARTNER