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in real materials behave in complex ways, and turning measurements back into quantitative information about a material’s internal state is a notoriously difficult inverse problem. This is where AI can
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with familiarity with foundation models (e.g. vision language models) and the ability to design and prototype innovative, reliable and reproducible solutions for complex 3D scene understanding tasks
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design of complex physical systems. The group is jointly embedded at Saarland University and DFKI in Saarbrücken. Current Research Directions include: AI for scientific computing – neural operators and
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | about 1 month ago
model systems. Your profile Master’s degree (or equivalent) in Physics, Applied Mathematics, Mechanical Engineering, or a related field Strong interest in fluid dynamics, turbulence, and nonlinear
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experimental approaches with computational modeling to investigate how RNA-mediated alterations contribute to early neurodegeneration in Alzheimer's Disease. The student will use iPSC-derived models and single
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metrology tools is continuously challenged by stricter requirements and increasingly complex geometries. In this project, you will work on the ideation, modeling, and experimental validation of novel
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, Heidelberg and Mannheim, our researchers harness interdisciplinary collaboration to decipher the complexities of disease at the systems level – from molecules and cells to organs and the entire organism
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therapy resistance has already been observed in both patients and pre-clinical mouse models, highlighting the urgent need to define the mechanisms that drive resistance and to identify rational combination
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environments, and distributional shifts. The research will combine probabilistic modelling, network-based representations, and modern AI methods to enable scalable and interpretable decision-making in complex
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protoplanetary discs matched to our nearest star-forming regions* Modelling outbursts in young stars during star cluster formation* Modelling how dust grows from micron-sized grains to km-sized planetesimals in