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without neurons in physical systems, Ann Rev Cond Matt Phys14, 417 (2023) [4] Dillavou, Beyer, Stern, Liu, Miskin and Durian, Machine learning without a processor: Emergent learning in a nonlinear analog
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to: Development of computer models of the musculoskeletal system incorporating neural control pathways Train NMS model control policies via RL Your seconday tasks will include: Using learned NMS model control
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? No Offer Description Job description Consortium This position is part of a European Doctoral Network consortium "Machine learning for integrated multi-parametric enzyme and bioprocess design", where 15
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Job description Consortium This position is part of a European Doctoral Network consortium "Machine learning for integrated multi-parametric enzyme and bioprocess design", where 15 doctoral projects
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work in our multi-disciplinary team, including AI-experts, MRI-physics experts, and clinical experts. You will conduct new academic research combining machine learning and medical physics. Your research
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We’re seeking for motivated candidates that are interested in developing computer models of the composite human neuro-muscular system that combine detailed musculoskeletal geometries, muscle-tendon
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clinical experts. You will conduct new academic research combining machine learning and medical physics. Your research will be published in reputable peer-reviewed journals. Additionally, you will regularly
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join us as a PhD candidate. You will work in a highly interdisciplinary group, at the intersection of physics, machine learning and theoretical neuroscience. Our group is focused on investigating
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» Chemical engineering Researcher Profile First Stage Researcher (R1) Country Netherlands Application Deadline 4 Jan 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Not Applicable Hours Per Week 38.0
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. Until now, specific EN fingerprints of localized corrosion are determined manually. This is a tedious procedure that requires considerable expert knowledge. Artificial intelligence or machine learning (AI