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be done via computer simulations, including Monte Carlo and molecular dynamics, combined with the use of statistical mechanics to predict e.g. phase transitions, nucleation rates, etc. The work will be
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be linked to other theses and postdoctoral research activities that use the results of this work and will also make contributions, for example on the evaporative response of plants. The regional
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 29 days ago
Framework Programme? Not funded by a EU programme Reference Number 2025-09322 Is the Job related to staff position within a Research Infrastructure? No Offer Description Context. This PhD thesis is part of
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, in accordance with regulations, requires that your arrival be authorized by the competent MESR authority. The proposed thesis is part of the ANR collaborative project ATHENA (Intelligent Automation
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science, or a related field. Strong background in quantitative methods and statistical analysis. Experience with computational tools for large-scale data analysis (e.g., Python, R, SQL). Familiarity with
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project is two-fold. First objective is to test if the explanatory variables are robust to the SDM approach, testing a suite of alternative correlative statistical models such as RANDOM forest, GAM
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Arts et Métiers Institute of Technology (ENSAM) | Paris 15, le de France | France | about 1 month ago
industrial process control remains under-explored; the current approach relies on statistical tests or conventional machine learning. One of the manufacturing processes addressed in this thesis is injection
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, Communication, Optimization • SyRI: Robotic Systems in Interaction The PhD student will join the CID team, whose research focuses on Artificial Intelligence, including statistical learning, uncertainty management
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for data collection, statistical analyses, manuscript preparation, and presenting results at international conferences. Main tasks include: - Implementing controlled breeding experiments to assess
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conditions. Profile: Required degree: Master's (M2) in Ecology and Evolution - Specialization: Modeling in ecology and evolution, theoretical ecology Expected skills: - Statistical analysis - Mathematical and