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large-scale training of scientific foundation models You will collaborate with researchers across machine learning, scientific computing, materials science, and data engineering, and work with leading
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to imagine novel task configurations and learn robust manipulation policies from just a few real demonstrations. You will work at the intersection of 3D computer vision, physical simulation, and robot learning
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to develop generative AI methods for nanoparticle drug delivery design, at the intersection of machine learning, explainability, and pharmaceutical nanotechnology. Job description We are looking for a
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and Liu, Supervised learning in physical networks: From machine learning to learning machines, PRX 11, 021045 (2021) [2] Stern and Murugan, Learning without neurons in physical systems, Ann Rev Cond
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for a Postdoc on Law, Disability, Sexuality & Technology 0,8 - 1,0 FTE Vacancy number 16381, to work on the NWO Vidi-funded project “Sex, Care, and Robots” for up to 48 months. Sexuality is a fundamental
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prestigious ERC Consolidator Grant in a great interdisciplinary institute in Amsterdam. Join us! Are you passionate about machine learning, natural language processing and generative AI? We are seeking a
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, the fusion community has started to develop fast surrogate models based on Machine Learning / AI models to speed up significantly the employed tools. Such tools have demonstrated to be generally applicable and
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16 Jan 2026 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Educational sciences » Education Educational sciences » Learning studies Engineering
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models that provide evidence-based reasoning for mission-critical decisions. Explainable AI for mission-critical decision support: design interpretable machine learning architectures capable of offering
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. Background: You hold a PhD in Computer Science, Machine Learning, Electrical Engineering, Embedded Systems or related fields. Core Expertise: Strong expertise in Federated Learning and/or Continual Learning