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are looking for a highly motivated and dynamic PhD student for a position in the Translational Neuroscience team headed by Prof. Rejko KRÜGER. The Translational Neuroscience team's focus lies in
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through molecular dynamics, simulations, and benchmarks Active Learning in Configurational and Chemical Spaces Integrate uncertainty-aware MLFFs into active learning frameworks Explore automated dataset
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dynamics and biomolecular condensates contribute to PD co-pathologies in human midbrain assembloid models. The work combines advanced imaging, molecular biology, and functional disease modeling. Key
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datasets across broad chemical space Evaluate models through molecular dynamics, simulations, and benchmarks Active Learning in Configurational and Chemical Spaces Integrate uncertainty-aware MLFFs
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as part of the CET (Comité d’Encadrement de Thèse) and will be daily supervised by the Postdoc employed in the project by UL. The PhD candidate will join the Mobilab Transport Research Group, a dynamic
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The successful candidate will pursue doctoral research in group actions, geometric structures, and smooth dynamics under the supervision of Prof. K. Melnick, within the Department of Mathematics
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Designing hierarchical graph‑based models to predict toxicity under uncertainty by linking molecular‑level and system‑level knowledge Advancing causal inference methods to predict transformation products
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carbon below the surface to sustain roots and symbiotic mycorrhizal fungi, forming a dynamic exchange network that governs water and nutrient uptake. Despite its importance, this underground system remains
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We are seeking a highly motivated PhD student to perform fundamental research and to conceive truly sparse solutions (on both, CPU and GPU) for dynamic sparse training, aiming to cut the training
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occurring below ground. Trees allocate a substantial proportion of their carbon below the surface to sustain roots and symbiotic mycorrhizal fungi, forming a dynamic exchange network that governs water and