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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 2 months ago
applied basic research into the protection of people and the environment from the effects of radioactive radiation. The Department of Structural Materials is looking for a Postdoc (f/m/d) Mechanical
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, calibration and reliability of large pre-trained models Probabilistic generative models and world models Probabilistic machine learning for scientific discovery Don’t see your exact idea listed? We encourage
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at the atomic scale. What you will do As device dimensions continue to shrink, surfaces and buried interfaces increasingly define performance, reliability and long-term stability. This postdoctoral position
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that are simple and efficient enough for parametric design, yet reliable enough to be trusted? Can a coupled engineering-level model reliably reproduce the interaction between clay and lining, turning high-fidelity
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of the Institute of Inorganic Chemistry. Supervision of students at all levels through scientific guidance, regular feedback, and structured mentoring. Teaching lectures on topics including environmental chemistry
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to advance the development and application of reliable, interpretable and uncertainty-aware machine learning methods for high-stakes regulated domains, including law, finance, policy and regulatory decision
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, XPS, and several additional chemical, structural, mechanical, thermal and electrical property analyses of experimental materials. Applicants for the position will preferably have advanced education and
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to advance the development and application of reliable, interpretable and uncertainty-aware machine learning methods for high-stakes regulated domains, including law, finance, policy and regulatory decision
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-driving and/or human-in-the-loop experiments; (3) computer vision for extracting patterns, structure, and meaning from images and/or volumes; and (4) new mathematics and algorithms that enable new reliable
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National Energy Technology Laboratory (NETL) | Albany, New York | United States | about 15 hours ago
will engage in multiscale modeling and machine learning to develop reliable, efficient framework for predicting long-term creep and fatigue behaviors of heat-resistant structural alloys considering