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parameters to identify regimes that ensure both flame stability and low pollutant emissions. Machine learning techniques have recently shown promise for Design of Experiments (DoE) and interpretation of large
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Open Positions DC 4: Use of machine learning tools for estimating EGs performance. Host Institution University Grenoble Alpes (France) Main Supervisor Alice Di Donna (alice.di-donna@univ-grenoble
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 25 days ago
Cancer Research Center. The team has access to several computing facilities (e.g. IGRIDA cluster) and established collaborations with other Inria/Irisa research teams in the field of machine learning. Our
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that are transforming many sectors today through language models, recommendation systems and advanced technologies. However, modern machine learning models, such as neural networks and ensemble models, remain largely
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FieldPhysicsYears of Research ExperienceNone Additional Information Eligibility criteria We are looking for a colleague with a PhD in particle physics. Experience with machine learning and/or experience with
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-driven drug development. Mentor junior team members and provide domain expertise to our R&D team. Bridge Research & Product: Translate biopharma research questions into actionable machine learning
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statistics and/or machine learning Specific knowledge • Proficiency in scientific computing • Knowledge of machine learning packages in Python or R • Proficiency in English (minimum level B2), as the postdoc
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innovations, OCTO Technology and the PIMM laboratory at ENSAM are jointly sponsoring this PhD thesis. The research will focus on the application of Physics-Informed Machine Learning (PIML) and Physics-Informed
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Research Infrastructure? No Offer Description The PhD will take place at LAMIH-UMR CNRS 8201, Université Polytechnique Hauts-de-France, Valenciennes, France. It is part of the JCJC ANR TeCAPE project
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), whose objective is to extend the HLA-Epicheck model, originally developed within the framework of a PhD thesis, and to implement new deep learning approaches to assess donor–recipient compatibility in