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language models (LLMs) and vision-language models (VLMs), you will investigate AI behavior, develop innovative evaluation methods, and translate scientific insights into real-world applications that improve
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QUINCY (Thum et al. 2019) into the Earth System Model ICON, and is currently using QUINCY for various ensemble-based projects including offline (i.e. reanalysis-driven simulations such TRENDY, NMIP) as
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Postdoc (f/m/d) Methods Development for the Micromechanical Characterization and Modeling of the ...
., finite element simulation, crystal plasticity modeling, analytical/semi-empirical hardening models, or data-driven analysis methods, along with a demonstrated ability to independently develop new
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 2 months ago
, analytical/semi-empirical hardening models, or data-driven analysis methods, along with a demonstrated ability to independently develop new methodological approaches # Experience with, or strong interest in
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8 Jul 2026 Job Information Organisation/Company Saarland University Department Data Driven Simulation and Analysis in Material Science Research Field Computer science Researcher Profile Established
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05.07.2026, Academic staff Our research combines mathematical modeling, numerical simulation, scientific computing, and data-driven methodologies to improve the predictive capabilities and
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team at the interface of environmental modelling and data science. At Hereon, the research group combines: - long-standing expertise in air quality modelling - with growing activities in data-driven
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Potsdam-Institut für Klimafolgenforschung e.V. (PIK) | Potsdam, Brandenburg | Germany | about 2 months ago
of integrated assessment and political futures modelling, starting on 01.11.2026. The position is funded for 2 years and 2 month until 31.12.2028. Renumeration is in accordance with the German public tariff
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-learning approaches. Projects may also include detailed molecular and functional characterization of particularly exciting candidate evolutionary changes using organoid and mouse models, CRISPR-based
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exploration and optimization of the process parameter space, as well as for adaptive, data-driven machine learning approaches to map the electrolysis process to a digital twin. In parallel, data workflows and