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-France 75 005, France [map ] Subject Areas: Statistical Physics Machine Learning Appl Deadline: 2026/01/16 04:59 AM UnitedKingdomTime (posted 2025/11/04 05:00 AM UnitedKingdomTime, listed until 2026/05/05
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statistical inference, machine learning and population genetics. The expected outcomes include new computational tools for studying B cell evolution, insights into age dependent immune diversity and the
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% with Laura Cantini’s team and 20% with the Bioinformatics and Biostatistics HUB. Information about the teams : The Machine Learning for Integrative Genomics team : https://research.pasteur.fr/en/team
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to structured programming in C++ and Python - knowledge of linux / unix operating system - fluent knowledge of spoken and written English - fundamental knowlegde of machine learning (and statistics) - good level
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team (https://research.pasteur.fr/en/team/machine-learning-for-integrative - genomics/) at Institut Pasteur, led by Laura Cantini, works at the interface of machine learning and biology (tools developed
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, statistics and probabilities • data science, machine learning, artificial intelligence • optimisation • power system management, integration of renewables • energy forecasting Expected level in french : bon
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processing, neuromorphic engineering, or a closely related field. A solid background in machine learning is expected, with interest or experience in spiking neural networks, temporal modeling, or bio-inspired
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, and/or multiphysics modelling • Mathematics & AI: Numerical analysis, inverse problems, neural networks, scientific machine learning • Programming: Python (scientific computing, ML), preferably C
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French National Research Institute for Agriculture, Food, and the Environment (INRAE) | Montpellier, Languedoc Roussillon | France | about 1 month ago
with skills in statistics and modelling through machine learning (Agronomy joint research unit, MIA joint research unit, Wageningen University & Research); (ii) national and international laboratories
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | about 2 months ago
modules leveraging deep learning for classical problems such as segmentation and 3D object tracking interfacing machine learning code and the robot using ROS2 contributing to the creation of datasets