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, France [map ] Subject Area: Machine Learning / Machine Learning Appl Deadline: 2025/12/13 04:59 AM** (posted 2025/10/21 05:00 AM, listed until 2026/04/22 04:59 AM) Position Description: Apply
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, Dark Matter and Axion Physics , Elementary Particle Physics , hep , hep-ph , HEP-Phenomenology (hep-ph) , High Energy Physics , High Energy Theory , Machine Learning , Neutrino physics , Particle
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scientific machine learning. Experiences in large language models, molecular modeling and simulations, and/or polymer science are preferred but not required. Applications should be submitted online
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of natural language processing, machine learning, artificial intelligence, and human-computer interaction. Established within the School of Computer Science, LTI pioneers innovative approaches to understanding
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phenomenology, applications of machine learning to particle phenomenology, and lattice QCD, both within the Standard Model and beyond. The particle physics phenomenology group members are: J. F. Kamenik (head), B
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Artificial Intelligence (AI) and Mathematics. This position involves applying machine learning tools, particularly reinforcement learning (RL), to address complex research-level mathematical problems and
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a two-year research project in collaboration with international academic partners. The main objective of the project is to study the interaction between machine learning and wireless communication
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ICT Services & Applications. Your role This position sits at the interface of machine learning, uncertainty quantification and computational biomechanics. You will work within the Legato group and in
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original research on clandestine printing networks using computational tools Contribute to publications in both AI and humanities venues (machine learning conferences and book history journals) Contribute
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Polytechnic University (UM6P), Benguerir, Morocco, is seeking for a postdoctoral candidate in the area of machine learning for IoT networks. The candidate must hold (or about to complete) a PhD in the related