Project for Bachelor or Master thesis

Everybody asks for explainable AI – you can contribute

Artificial intelligence and different approaches to machine learning and deep learning currently create a lot of hope and have proven successful in data sorting and pattern recognition. Here, we want to employ the principles of SciML (https://www.sciml.ai) and the Julia ecosystem for AI and Deep Machine Learning to use data and physical principles to learn mathematical models of dynamic cellular processes such as metabolic reactions or signaling networks. Based on initial work of our and collaborating groups, you will initially investigate typical small models (such as for an enzymatic reaction or few steps in a signaling cascade) and ask which type and quantity of data would allow to learn the unknown kinetics. Then, the investigation will be extended to networks of relevance for our current projects in metabolic modeling and comprehensive modeling of signaling networks.

Requirements: Interest in mathematical modeling of cell processes, basic programming skills, interest in challenging biological questions. Given teaching content, biophysics students would have the required background.

Skills you will acquire: Learn how to formulate testable scientific questions. Improve your programming skills, especially in Julia. Cooperation in a highly dynamic field with many applications in biology and beyond.

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