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Field
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; they make sense to humans and are accessible to algorithmic techniques while neural models are adaptive and learnable. The aim of this project is to develop models which combine these advantages. The project
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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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28 Aug 2025 Job Information Organisation/Company CNRS Department Laboratoire d'informatique de modélisation et d'optimisation des systèmes Research Field Computer science Mathematics » Algorithms
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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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for accurate and efficient analysis. Possible Research Themes We seek motivated PhD candidates to advance computational mass spectrometry in one or more of the following directions: Algorithm Development Design
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-world error patterns: we therefore aim to go beyond the traditional depolarizing error model generally accepted in the quantum coding community. One therefore wishes to adapt existing decoding algorithms
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ERC project FoRECAST, we aim to develop theory (e.g., new probabilistic and differential inference algorithms as well as proofs of their correctness and efficiency) and systems (e.g., high performance
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremen, Bremen | Germany | 3 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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decision support algorithms in clinical practice. Additionally, the project involves collaborations with large industrial partners such as Roche Diagnostics and SISCAPA. Through this collaboration, you will
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to have good knowledge of computer science, mathematics, algorithms, and programming. Knowledge and experience in artificial intelligence and machine learning is expected, but not required. Knowledge and