PhD Student (65% E13, 3 years) Graph-based machine learning of disease networks

Our group aims to elucidate the molecular mechanisms behind phenotypes in general and human diseases, in particular. To that end, we develop integrative bioinformatics methods leveraging network analysis, machine learning techniques, and statistical approaches. We apply own and existing approaches in close collaboration with biologists and physicians to derive insights from multi-omics data.

Activities and responsibilities

Project description
Diseases are currently defined and diagnosed based on symptoms (e.g., hypertension, depression) or affected organs (e.g., heart failure, nephropathy). Clearly, there is a lack of mechanistic understanding of diseases that could improve disease definitions and allow for more informed selection and development of therapies. To achieve such understanding we develop integrative bioinformatics methods leveraging network analysis, machine learning techniques and statistical approaches. Your work will focus on the development of graph-based machine learning techniques to identify disease- and patient-specific dysregulated subnetworks.

Qualification profile

You bring
  • Degree in bioinformatics, molecular biology, computer science, or similar
  • Solid understanding of molecular biology
  • Experience with omics data analysis, graph analysis, machine learning
  • Strong programming skills in Python, R, and / or Java
  • Familiarity with Linux and HPC environments
  • Fluency in English in written and spoken language
  • Strong commitment and motivation, ability to work collaboratively

Benefits

We offer

At the Chair of Experimental Bioinformatics you will find a supportive and productive research environment with a young, dynamic team of more than 20 international researchers at different stages in their career and education. Furthermore, you will have the chance to participate in a large EU-wide project and travel to international meetings with other scientists.

Send your application including your CV, grades, publications (if any) and a reference letter to tim.kacprowski@wzw.tum.de.

Send application to

*Dr. Tim Kacprowski*
Research Group Computational Systems Medicine
Chair of Experimental Bioinformatics
TUM School of Life Sciences Weihenstephan
Technical University of Munich
Maximus-von-Imhof-Forum 3
85354 Freising
Germany

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About TUM - Experimental Bioinformatics

The Technical University of Munich (TUM) is one of Europe’s top universities. It is committed to excellence in research and teaching, interdisciplinary education and the active promotion of promising young scientists. The university also forges strong links with companies and scientific institutions across the world. TUM was one of the first universities in Germany to be named a University of...

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