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Field
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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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environmental geophysics. This PhD project aims to advance the process-based understanding of SSF by combining state-of-the-art geophysical methods with controlled field experiments and numerical modeling
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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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7 Apr 2026 Job Information Organisation/Company Technische Universität Dortmund Department Department of Physics Research Field Physics Computer science Geosciences Mathematics Engineering
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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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HBIGS Heidelberg Biosciences International Graduate School | Mannheim, Baden W rttemberg | Germany | 17 days ago
Starting Date 1 Jul 2026 Is the job funded through the EU Research Framework Programme? Other EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Earth’s
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) Expertise in further programming languages (in particular C++), GPU programming, parallel programming or high-performance computing are highly valued Keen interest in neuroscience is essential Experience with
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individuals holding a Master of Science degree in condensed matter physics or materials science, with a focus on numerical simulations - Prior experience with theoretical and or computational project work is
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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