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and lifetime validation. This position is part of a twin-PhD collaboration between Forschungszentrum Jülich (FZJ) and the French Alternative Energies and Atomic Energy Commission (CEA). The two projects
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, or computer science. Core competencies: solid background in quantum many-body physics strong programming skills (Python required, Rust a plus) experience with tensor networks, variational Monte-Carlo, machine learning
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archaeological and historical contexts is also required. Additionally, the ability to perform *ad hoc* data processing (multivariate statistics, machine learning, etc.) is desirable. Proficiency in programming
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to correlate polymerisation kinetics, macromolecular architecture, morphological evolution and drug encapsulation mechanisms. Beyond experimental work, the project will integrate machine learning approaches
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in all these approaches. This PhD position aims at developing methods for learning and control in complex large-scale systems. This will be carried out as part of the GreenControl project, whose
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
partners (Inria, CEA, CNRS, etc.) and validate your research on real-world industrial use cases. Join a network of PhD candidates within the EDT program, fostering collaboration, peer support, and
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, machine learning, explainable artificial intelligence (XAI), digital twins, and integrated data-model approaches. • Study of the frugality of the developed approaches by reducing the requirements