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associate in the broad areas of high performance computing and machine learning. HighZ is focused on developing scalable high order methods, enhanced with surrogate models for subscale physics, for modeling
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machine learning (ML) approaches offer a powerful framework for modeling complex catalytic materials with near ab initio accuracy while enabling simulations at significantly larger spatial and temporal
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implement machine learning models dedicated to the prediction, interpretation, and quantitative analysis of Raman vibrational spectra, establishing explicit links between structure, local chemical environment
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reproducible analysis workflows Familiarity with computational models of vision and machine learning methods (for example CNNs, deep generative models, encoding models) is preferred but not required Ability
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of: • machine learning • cybersecurity • distributed systems • privacy-enhancing technologies The research will be carried out within the (team name) at LS2N, focusing on trustworthy AI and cybersecurity
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electrophysiology data obtained through collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in
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in solid mechanics framework Experience in non-linear solid material response and fracture modeling Experience in machine-learning modeling for solid mechanics applications Experience in
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research projects will be considered.) Technical expertise in machine learning and model fine-tuning – 10% Demonstrated experience with neural network training, loss function design, embedding-based models
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integrates spatio-temporal analyses (including synthetic descriptions such as distribution envelopes, size structures, and joint species distribution modeling), trophic modeling, and machine learning
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on novel applications of machine learning techniques on mobility data towards resilient, safe, inclusive and sustainable urban micro-mobility systems. You will become member of an international, 38-partner