4,766 model-driven-development "Integreat Norwegian Centre for Knowledge driven Machine Learning" Postdoctoral positions
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candidate will join the Scientific Machine Learning group at TDB and SciLifeLab. The group develops theory, methods and software for data-driven science, with a current focus on uncertainty quantification
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expertise in cognitive zoology, neurobiology, and palaeontology to understand how cognition and brains have evolved in concert over eons, and what has driven this evolution. By working together, we
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | 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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empirical taxonomy developed by the PhD candidate. Model training and fine-tuning. Fine-tune large language models on annotated educational datasets collected during the project, ensuring the agent's
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to use data and modelling to make historic buildings energy efficient – without compromising their cultural value? Join the research project DASHER at KTH. DASHER (Data-driven Structured Historic Energy
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the Biological, Environmental and Planetary Science Division. Advance the frontiers of structural biology by developing AI-driven methods for interpreting complex crystallographic data. This role will enable new
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National Energy Technology Laboratory (NETL) | Albany, New York | United States | about 11 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
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on the HASRL framework and the empirical taxonomy developed by the PhD candidate. Model training and fine-tuning. Fine-tune large language models on annotated educational datasets collected during the project
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and AI methods for materials discovery. Design and implement active-learning workflows for autonomous exploration of materials spaces. Develop and evaluate generative AI models for inverse materials
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, Environmental and Planetary Science Division. The aim of the position is to advance the frontiers of structural biology by developing AI-driven methods for improving crystal quality at scale. This role will