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conceptual DFT (linear response function, Fukui functions) or QTAIM theory (delocalization index), and their validation on a set of compounds known from the literature - interfacing a MLIP (Machine-Learned
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multidisciplinary experience. Knowledge in applied computer science, particularly in machine learning; in fluid mechanics, especially in hydrodynamics; and in electronics, particularly in instrumentation 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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dynamical systems), epidemiological modelling, data analysis (statistics, machine learning). • in scientific programming (preferably Python, Matlab, R) Genuine interest in the analysis and modeling
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Eligibility criteria Instrumental optics and imaging (microscopy, camera detection) for biology. Skills in coding and experiment control. Basics of machine learning and/or signal processing. Teamwork
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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
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and machine learning applied to data fusion and adapt them to the field of exoplanet characterization. They will develop and maintain the FORMOSA code in coordination with the team of students working