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structure prediction. Working at the intersection of generative AI and biophysics, the Fellow will focus on expanding the current framework to model dynamic protein ensembles. As an Empire AI-funded fellow
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for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein ensemble structures. As an Empire AI-funded fellow
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at CNRS in the Institute of Chemistry of Media and Materials of Poitiers (IC2MP - UMR CNRS 7285 - https://ic2mp.labo.univ-poitiers.fr/ ) in the Catalysis and Non-conventional media team, under
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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of the Arctic Ocean, to assess its reliability (do the predicted error bars encompass the actual errors?), its inclusion into ensemble data assimilation, and its use in operational forecasting. The PhD fellow
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning architectures (CNNs, RNNs). Experience with explainable AI (e.g., SHAP, LIME) is preferred
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, ensemble Kalman filters, and physics-informed neural networks (PINNs) enforce conservation laws while fitting observations. The key is to apply the vast amount of physical insights developed in turbulence
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by combining psychological profiling, biological lab data, physiological time series, and sensor data. The postdoc will play a leading role in developing and implementing predictive algorithms designed
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purely correlational analyses and to develop predictive models with operational relevance. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR8212-DAVFAR-008/Candidater.aspx Requirements