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technologies generate unprecedented volumes of molecular data at cellular resolution, opening new avenues for the application of machine learning to fundamental biological problems. The postdoctoral researchers
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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic
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molecular dynamics simulations of modified nucleosomes - analyze the large data set obtained using various analysis tools, from visualization to automation using machine learning tools - perform QM/MM
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conferences. • Contribute to the writing of scientific publications. Optional : • Design Machine Learning (ML) potentials. • Code in FORTRAN and PYTHON to improve the functionality of the global
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The postdoctoral researcher will participate in the construction and
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving
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computer scientist with experience in bioinformatics, solid programming skills and knowledge in 3D protein structures. Machine learning skills and knowledge of Web development are a plus. Good interpersonal
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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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Machine/Deep learning and classification Knowledge of the Linux operating system for using a computing cluster Interest in transdisciplinarity and teamwork Autonomy and scientific rigor Website