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FieldBiological sciencesEducation LevelPhD or equivalent Specific Requirements PhD in Physics, Computational Biology, Bioinformatics, or equivalent. Knowledge of biophysics, statistical physics, machine learning
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FieldComputer science » OtherEducation LevelPhD or equivalent Skills/Qualifications CANDIDATE ’S PROFILE The candidate should possess a PhD in machine learning or computer vision and have a strong publication
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valued. · Knowledge of chemical reactions and how to model them through computer simulations is highly valued. · Knowledge of classical molecular dynamics, including Machine Learning Interatomic Potentials
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. Familiarity with statistical modelling, machine learning and deep-learning Additional information: We offer: 🌐The opportunity to work with our state-of-the-art HPC infrastructure and to join a vibrant network
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apply statistical and machine learning models to identify predictive markers of depression. Design and execute experimental protocols related to self- representation and chronic pain. Prepare manuscripts
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in cutting-edge techniques, from detector R&D to advanced data analysis and machine learning. Attendance to international collaboration meetings, schools and workshops. Development of transferable
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. SILEX 2025) to calculate the Fire Radiative Power (FRP) and compare with satellite observations (VIIRS, SLSTR, FCI). Develop a fire front segmentation algorithm using machine learning techniques (deep
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow
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parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing; additionally other methods such as simulation-based inference Good computing skills
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow