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, matrix product state and neural quantum state methods). Some of our recent papers relevant to the position include: https://arxiv.org/abs/2411.15299 https://arxiv.org/abs/2506.22539 https://arxiv.org/abs
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 4 days ago
of the ERC Starting Grant DYNASTY (Dynamics-Aware Theory of Deep Learning). The position might include traveling to conferences for paper presentation. Travel expenses will be covered within the limits
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Università degli studi di Roma Tor Vergata Dipartimento di Biomedicina e Prevenzione | Italy | 7 days ago
on the results, supporting the project’s neuromorphic learning activities Where to apply Website https://pica.cineca.it/uniroma2/ Requirements Additional Information Eligibility criteria Eligible
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mechanics, information theory, stochastic processes, population dynamics, and neural networks. Within this framework, machine learning systems and artificial generative models will also be considered, where
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the project entitled ‘Methods for ensuring resilience in advanced software engineering environments’ Where to apply Website https://sede.uca.es/opencms/system/modules/sede/index Requirements Research
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, exact diagonalisation, matrix product state and neural quantum state methods, as well as quantum simulations). Some of our recent papers relevant to the position include: https://arxiv.org/abs/2011.01955
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exact diagonalisation, matrix product state and neural quantum state methods, as well as quantum simulations). Some of our recent papers relevant to the position include: https://arxiv.org/abs/2607.26156
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DIRECTOR OF SERVICE LEARNING Requisition # 76062 Position Basics Type of PositionCLINICAL-TRACK Advertising Ends on:Extended Until Position is Filled Advertising Started on:Friday, October 9th, 2026
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the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Machine learning, Real algebraic Geometry
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new theoretical approaches for understanding stability, generalization, and feature learning in large-scale neural networks. Further details and application instructions are available at: https