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position (contract-based) and one PhD fellowship in Computational Biophysics/Chemistry (see also https://constructor.university/comp_phys). The PhD position is focused on efficient algorithms
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this project, you will develop innovative quantum algorithms for the accurate calculation of materials properties. You will combine methods from quantum informatics and solid-state physics to describe
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research in a variety of areas in biology and computation, with possible specializations in genomic and molecular biology techniques as well as in algorithms, statistics and artificial intelligence
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for traceability, robustness, and physicochemical correctness Implement and adapt the most promising algorithms to model radionuclide migration in crystalline host rocks Execute proof‑of‑concept ML simulations and
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
predictive machine learning model; iii) based on the machine learning algorithms, develop PBF-LB Mg alloy with defined microstructure, improved mechanical and corrosion properties. Research stays are planned
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Disse), the Chair of Geoinformatics (Prof. Thomas H. Kolbe), and the Chair of Algorithmic Machine Learning & Explainable AI (Prof. Stefan Bauer). The project aims to develop an integrated urban flood
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starting date is November 2025. The topic of the PhD project will be theoretical research in discrete optimization, with a particular focus on either graph algorithms or multiobjective optimization
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groups are strongly encouraged to apply. Your Tasks: Development and application of algorithms for modelling, evaluation and visualization of ultrafast processes Investigation of ultrafast dynamics in
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremen, Bremen | Germany | about 2 months ago
ecological questions. Your Tasks Develop and test novel forms of network-based feature selection for the application of ML algorithms to marine microbial eDNA and eRNA datasets, integrating a range of
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algorithms to compute similarity between interaction interfaces across millions of comparisons. This hinders identification of novel modes of protein binding, i.e. those predicted by AlphaFold, and it hinders