Description

With the ongoing digitalization in the medical field and the improvement of new technologies like DNA-sequencing, an increasing amount of health data is generated every year. The resulting datasets have the potential to not only deepen the understanding of biological processes in general, but also to guide the medical practice toward more individualized and effective treatment- and prevention programs.

A key step to use that potential is the development of new algorithms and models to efficiently work on big and diverse biomedical datasets, using machine learning among other techniques.
Theses in the context of this research topic are available.


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Requirements

• Skills in C/C++ or Python programming (depending on the specific task or topic)
• Ability to work independently
• Interest in the field of bioinformatics
• At least basic experience with machine learning frameworks

Contact person: Fabian Müntefering