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The development of statistical/machine learning approaches for downscaling at the kilometer scale will be the main mission of the position. For various climate variables (temperature, precipitation, wind, etc
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molecular simulations, machine-learning techniques, and statistical mechanics for research opportunities in: Development of data-driven schemes for the discovery of slow degrees of freedom Molecular
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human cohorts would be a plus. Minimum level of training and/or experience required : Doctorate in Computer Science on a topic related to machine learning or deep learning Research FieldComputer science
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sequencing), three hospitals (each having a vast biobank of lung cancer clinical samples) and a computational group in machine learning for precision oncology. The postdoc will report directly to the leader
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Postdoctoral position (M/F): Machine learning design of alloys for concentrated solar energy storage
mission will be to develop machine learning models to predict properties of alloys of elements of groups 1 to 15, such as their melting temperature, range, and enthalpy. Based on these predictive models
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omics tumor profiles, and proceed by sophisticated machine learning approaches as well as biological network modelling. At Institut Curie our situation is ideal to pursue these goals, since the choice
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approach and framed as a continuous improvement process, and (3) on machine learning algorithms guided by theory and analogues from natural objects and simulations. The proposed position will cover four
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of learning dynamic systems and physically informed neural networks (PINNs), for application to neuroscience research. The main task of the postdoctoral fellow will be to develop models for modeling
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with interests in molecular simulations, machine learning, and statistical mechanics for research opportunities in: • Development of data-driven schemes for the discovery of slow degrees of freedom
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machine learning have transformed our approach to inverse problems in various fields, notably in medical imaging, enabling a deeper understanding of complex data structures. However, although sophisticated