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. Your core research tasks include: Developing advanced MBS/EHD models for drivetrain systems, incorporating transient tribological changes. Creating machine-learning-based surrogate models to enable rapid
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Job related to staff position within a Research Infrastructure? No Offer Description PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models for complex data, including temporal data
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
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an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology Machine Learning, AI Safety, AI Alignment, Eval of LLMs, Multi-agent Safety
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, certification, and/or registration. Specific knowledge, skills and abilities required to perform the job satisfactorily include: Demonstrated experience developing and/or implementing machine learning models in
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Networks. Knowledge of and experience in Python, TensorFlow, Keras, or other Machine Learning toolboxes, is essential. Knowledge of and experience in Large Language Models is highly relevant. The successful
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | about 15 hours ago
"Cloud physics" (f/d/m/x) Background With the project Deepcloud, we will use the machine-learning revolution to better understand clouds and their role in the climate system. We aim to train a deep
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | 3 days ago
) the exploration of mixed-precision arithmetic in the context of high-order discontinuous discretization methods, and (2) the integration of machine learning techniques to complement and enhance traditional
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significant modeling challenge. This project addresses that challenge by combining machine learning with constitutive modeling, while ensuring adherence to physical laws. Although the primary focus is on large