73 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S" positions in France
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, machine learning (PINN, supervised learning) - Python/PyTorch programming - Autonomy, curiosity, and adaptability - Excellent writing skills Specific Requirements The doctoral student's host laboratory is
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the creation of high-precision digital twins. Activity 1: Integration of Photometric Stereo in Meshroom - Implement processing nodes for normal field and intrinsic color estimation. - Integrate deep learning
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, Éducation en Océanie” [https://heliceo.huma-num.fr/ ] est un consortium scientifique du CNRS, prévu sur plusieurs années (2025–2030), et pour l’instant financé pour les 12 premiers mois. Il vise la
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for experimentation, yet they remain difficult to deploy directly onboard robots due to hardware availability, latency, sampling cost, and noise. Previous work on quantum machine learning (QML) emphasize
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significant computational component. We strongly recommend a background in machine learning and coding. Applicants with a background in areas such as computational neuroscience, reinforcement learning, or deep
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should have a graduate degree (Master 2 degree). Him/her scholar background should include: • statistical/machine learning, statistical inference, clustering, classification • deep learning, variational
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within the project AI4TECSWriting a doctoral dissertation in computer sciencePublishing research findings in leading international conferences and high‑impact journals in AI, machine learning, and
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strong background in optimization and machine learning. Good coding skills in Python, PyTorch are welcomed. Application Applications should contain a CV, a motivation letter, the grade records of the last
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, biology, computer science or related disciplines Strong computational skills, including machine learning, e.g. demonstrable project in a relevant field A strong first-author publication record in a relevant
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water matrices and case studies, while the AI4Science PhD will develop machine‑learning models that learn from and build upon these pNTA results. The successful candidate will be supervised by Prof. Dr