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, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s mission is to establish an internationally
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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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world where AI systems are reshaping how we learn, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI
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-resolved prediction of wave elevation and wave loads in continuous time. Look into fusing the models into a foundational model. Setting up a synthetic training gym as a platform for AI model learning, e.g
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support each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In the FlexMobility project we propose a holistic
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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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for data augmentation, small language models, anomaly detection using learning methods, and explainability techniques for decision-making. The research will involve designing and developing prototypes
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, machine learning and behavioral modeling methodologies. In the FlexMobility project we propose a holistic approach to design a public transport network that includes both traditional fixed lines and
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performance in fuel cell (biogas) and co-electrolysis applications. To achieve this, you will employ computational fluid dynamics (CFD) and machine learning (ML) to investigate degradation mechanisms under
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applications. To achieve this, you will employ computational fluid dynamics (CFD) and machine learning (ML) to investigate degradation mechanisms under various operating conditions and develop strategies