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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 1 month ago
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 employees
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) for municipalities; Optimizing building-level energy demand and supply Extend and customize the frameworks according to the research questions and project needs. Conduct scenario analyses to explore pathways
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, their modelling through mathematics and numerical simulations, and their control and optimization. Our belief is that a proper understanding of systems requires a modelling step, which allows to identify causal
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quite heavily. We establish new results concerning the analysis of stochastically driven anisotropic fluids, design novel numerical simulation and optimal control schemes, and provide new means for risk
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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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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 12 days ago
measurements of growth and expression levels of ribosomes and enzymes in the model organism Escherichia coli [5]. The proposed postdoc position involves the numerical simulation, analysis and inference
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ocean environments, ensure safe and sustainable operations. Our activities are centered on numerical modelling (e.g. CFD, FEA, FSI, optimization, machine learning), but also include experiments and real
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on classical supercomputers. Design and optimize classical simulation algorithms for quantum circuits. Explore approximate classical simulation algorithms (e.g., tensor networks, circuit cutting) to improve
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(e.g., superconducting quantum processors). Validate, analyze, and interpret experimental data and results. Simulate the performance of quantum circuits on classical supercomputers. Design and optimize
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to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal