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Engineering » Mechanical engineering Engineering » Other Mathematics » Applied mathematics Physics » Computational physics Physics » Solid state physics Physics » Thermodynamics Researcher Profile First Stage
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15 Apr 2026 Job Information Organisation/Company Sorbonne University, IMPMC-UMR 7590 Research Field Computer science » Informatics Biological sciences » Biology Physics Researcher Profile Recognised
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, evaluate the selected parameters and use simulation to find out the values of the contact process parameters. The contribution of the dissertation will be the evaluation of the obtained theoretical and
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2026 - 21:59 (UTC) Country Sweden Type of Contract Not Applicable Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
To strengthen our team in the division 7.7 “Modeling and Simulation” in Berlin-Steglitz, starting 01.07.2026, we are looking for a Research Assistant (m/f/d) with a Ph.D. in Civil Engineering
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adaptation without requiring additional simulations. 3- RESEARCH PROGRAMME, METHODOLOGIES AND MEANS ----------------------- -Phase 1 (Months 1-12): Stability analysis using LightKrylov and LightROM
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. Copies of the supplements to the educational documents give information on the content, scope, and grades of the completed program. A copy of the identity document. Letter of Concurrence in the form
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. Copies of the supplements to the educational documents give information on the content, scope, and grades of the completed program. A copy of the identity document. Letter of Concurrence in the form
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programme that will train the next-generation of doctoral carbon champions who are renowned for research excellence and interdisciplinary systemic thinking for Net Zero. The ReNU+ vision is that they will
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research program that brings together physics, chemistry, and machine learning. Your research tasks will include: Uncertainty Estimation in Deep Neural Networks for MLFFs Implement and test uncertainty-aware