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computational processes, improving prediction accuracy, and enabling the creation of extensive model ensembles at a reduced cost. In this context, we are looking for a highly motivated postdoctoral researcher
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École nationale des ponts et chaussées | Champs sur Marne, le de France | France | about 1 month ago
combining multiple ML models have been explored to optimise predictions, enabling algorithms to collaborate and achieve better results. Ensemble methods, in particular, have demonstrated superior performance
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/unsupervised learning (regression, classification, clustering), ensemble methods, and deep learning architectures (CNNs, RNNs). Experience with explainable AI (e.g., SHAP, LIME) and radiomics preferred
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emulators of multiple ice sheet and glacier models, using ensembles of simulations, in collaboration with the project scientists, with the purpose of delivering projections for the land ice contribution
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combination of applied lessons for students, end of semester juries, recitals, classes, ensembles, etc. as determined by the Director. MINIMUM QUALIFICATIONS: Experience in collaborative piano. POSITION
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learning “emulators” of multiple ice sheet and glacier models, based on large ensembles of simulations extending to 2300. The simulations will be from two international projects aiming to inform
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Max Planck Institute for the Structure and Dynamics of Matter, Hamburg | Hamburg, Hamburg | Germany | 3 months ago
of multiple timescales. Collectively induced stochastic resonance phenomena on molecular ensembles in optical cavities. Cavity-induced off-equilibrium consequences on chemical reaction rates Develop (ab-initio
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. Empa is a research institution of the ETH Domain. Empa's Laboratory of Biomimetic Membranes and Textiles is a pioneer in physics-based modeling at multiple scales. We bridge the virtual to the real world
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combined with modeling experiments or enhanced research in parameterization, AI is accelerating computational processes, improving prediction accuracy, and enabling the creation of extensive model ensembles
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models (e.g. tumour progression, tumour-drug sensitivity, survivability) by integrating multiple and heterogeneous data with associative data mining and ensemble learning methods.