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, 100% funded PhD student position to fill starting around June 2026. Research is to be in the field of computational methods in nonlinear and large scale optimization / inverse problems or in novel
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for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics-aware learning methods with domain decomposition techniques, enabling parallel training and
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Germany Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research
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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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. The future junior professor is expected to address key questions in the area of numerical methods for partial differential equations. Participation in the research activities of the Faculty of Natural Sciences
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Your Job: This PhD project bridges between classical analytical methods and modern AI based techniques to analyse spike train recordings to advance our understanding of neural population coding
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a related discipline Experience with surface X-ray scattering methods Experience using synchrotron radiation facilities Experience in catalysis and recycling Knowledge of interfaces science Knowledge
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systems for measurement processes, or innovative hardware and software for quantum computers and quantum sensors: we develop tomorrow’s technology in-house for a sustainable and secure society. Become part
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from over 50 nations, it is the largest institute of the Max Planck Society. The Department of Theoretical and Computational Biophysics (Prof. Dr. Helmut Grubmüller) is inviting applications for a PhD
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GPU-capable, parallelized simulation frameworks. Work closely with experts in HPC and power systems to enhance scalability and computational performance. Disseminate your findings through scientific