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.
- 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
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.
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