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. The successful candidate will integrate multiscale first-principles modeling, sparse symbolic regression, and adaptive experimental design in self-driving laboratory settings to elucidate both desired catalytic
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, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
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of China, Sub-projects of the National Science and Technology Support Program, the National Spark Program, and Key R&D programs in Hainan, Guangxi, and Jiangsu provinces, as well as industry-funded
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planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and manipulation strategy adaptation Real-world
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or Postdoc Position in Numerical Mathematics m/f/d, 100%, 2 years+ As part of the second phase of the DFG funded Priority Programme SPP2311, the Chair for Numerical Mathematics under the leadership
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] Subject Areas: Applied Mathematics, numerical methods, simulation and modelling Appl Deadline: 2025/05/31 11:59PM (accepting applications posted 2025/02/13) Position Description: Position Description
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scaling and generalization behavior Roll out the model to the global user community Requirements PhD or MSc in computer science, physics, mathematics or a related discipline Experience with large-scale HPC
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photoreceptive pathways using mouse as a model system. Methods used in the lab include various in vitro, ex vivo and in vivo approaches, as well as functional and behavioral studies of relevant opsin knockout mice
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`liquid-liquid phase separation' (LLPS). We will use programmable, multi-component model systems of biomolecular phase separation to investigate the transport of biomolecular information, stress, and light
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topic from the areas represented within the group. The group’s interdisciplinary focus includes not only classical topics in numerical analysis, such as the analysis of nonlinear PDEs, but also modeling