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optimization or discrete algorithms. Profound mathematical modeling and programming skills. Experience with the design and analysis of graph algorithms or multiobjective optimization models is a plus. Very good
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its rich information content, conventional analysis methods have not yet fully realized its potential. This research project aims to develop a robust AI foundation model based on modern Transformer
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or microfluidics and ideally in quantitative data analysis. As a theoretical candidate, you have knowledge in quantitative biology, soft matter/complex systems physics or statistical physics. You enjoy working in
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13.01.2020, Wissenschaftliches Personal PhD position at the Chair of Algorithms and Complexity. Candidate shall work on approximation algorithms for scheduling problems in parallel and distributed