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-of-the-art Pt-Ru will proceed in stages: beginning with foundational machine-learning potentials such as NequIP, MACE, SevenNet, or Orb-V3, followed by progressive validation of predictions via DFT
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artificial intelligence and machine learning tools for data analysis and materials prediction, this strategy offers a significant acceleration in the discovery and optimization of functional materials
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Inria, the French national research institute for the digital sciences | Gif-sur-Yvette, Île-de-France | France | 19 days ago
/Qualifications Proficiency in the Python programming language and the PyTorch or TensorFlow environment is required. Experience in machine learning / neural networks is strongly recommended. Candidates
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leader in the digital world. We are seeking an Assistant/Associate Professor to join the Signal, Statistics, and Machine Learning [ S2A ] team in the Image, Data, and Signal (IDS) department. You
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Research Field Physics Years of Research Experience None Additional Information Eligibility criteria - Education: Ph.D. degree in Robotics, Control, Optimization, Machine Learning, Computer Vision for
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insights are further complemented by machine-learning-assisted molecular dynamics simulations performed by a partner laboratory. For the methodological development, we focus on oxides with increasing
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on metallic alloys and oxides, we will first develop and benchmark machine-learning interaction potentials of increasing complexity. Subsequently, we will deploy a combination of brute-force and rare
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through few-shot adaptation. Research Objectives The primary objective of this PhD is to develop a new generation of hybrid models combining large-scale machine learning with physical knowledge to
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high-performance GPU cards will be provided to the PhD student to support the development, training, and implementation of machine learning models. Travel and participation costs for project meetings and
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background noise (e.g., PMT dark counts, accidental coincidences). • Integrate machine learning techniques (e.g., boosted decision trees) to improve signal identification and background rejection