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position as POSTDOC (F/M/X) in the Optimization and Optimal Control Group . (Full-time employee) for an initial period of one year, with starting date to be arranged. Your Tasks The full-time position is
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numerical validation of error bounds for model-order reduction based on optimal transport, in the context of electronic structure calculations. The postdoctoral researcher will be assigned to the Besançon
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 22 hours ago
: 01.01.2026 Job description:Postdoc (f/m/d) AI-based automated adaptation and biological optimization of proton therapy With cutting-edge research in the fields of ENERGY, HEALTH and MATTER, around 1,500
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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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at the intersection of numerical linear algebra and advanced HPC. The candidate will join an international environment, with opportunities to collaborate with experts from the USA, and KAUST and publish in top-tier
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” focusing on the effect of a fluctuating environment on the collective dynamics of self-propelled agents, a numerical part on “reinforcement learning” focusing on optimizing communication between agents in a
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, and network optimization for 6G networks and FutureG wireless networks. Successful candidates will have the chance to work with top-notch researchers from both academia and industry on future wireless
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until 31.12.2028 Remuneration according to TV-L The Göttingen Campus is hosting numerous prestigious and internationally renowned research institutions. This includes the University and its Medical Center
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[map ] Subject Area: Applied and Numerical Analysis Appl Deadline: 2025/09/15 11:59PM (posted 2025/08/05, listed until 2025/12/31) Position Description: Apply Position Description Doctoral and
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simulations and multiscale spatial-omics data. • Integrate uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments