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Field
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to reduce the cost of clean hydrogen to $1/kg by 2031. The project proposes to address key scientific challenges by using molecular simulations (reactive force fields like ReaxFF and machine learning
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computation of the corresponding Gröbner basis becomes almost immediate. We will investigate structural heuristics, exploration of the Gröbner cone, and, more speculatively, machine learning approaches
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Role : The Machine Learning for Integrative Genomics team, led by Laura Cantini, is recruiting a permanent research engineer. The successful candidate will carry out single-cell data analysis in
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time scales. To do this, we will build on a landscape picture of stochastic gene expression dynamics inferred from data using modern machine learning techniques. The results will inform us about how
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integrates spatio-temporal analyses (including synthetic descriptions such as distribution envelopes, size structures, and joint species distribution modeling), trophic modeling, and machine learning
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problems, statistical learning and machine learning (machine learning, deep learning) - Knowledge of associated software development tools and environments: Python, PyTorch, Scikit-learn, Jax, Julia
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, computational neuroscience, bioinformatics, robotics, or a related field Strong expertise in computational data analysis (e.g., behavioral analysis, signal processing, or machine learning) Experience working with
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structure calculations, vibronic property simulations, and analyzing surface adsorption phenomena. Knowledge of machine learning potentials (e.g., GAP, ACE) or reactive force fields is a plus, as fallback
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new thermoelectric materials using data science and machine learning methods applied to materials, based on expert-reviewed experimental data from the literature and public databases (notably
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stochastic modeling, Bayesian inference, data fusion and modern machine learning. Its research activities span various application domains such as security, non-destructive testing, infrared imaging and