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Embodied AI Trustworthy AI, including explainability, auditability, and privacy Edge AI and model optimisation Physics-informed neural networks (PINNs) and surrogate modelling Time-series modelling and
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on Artificial Neural Networks and Gaussian Process modelling, to accelerate processing optimisation. Consolidate experimental, techno‑economic, and sustainability data into robust technical evidence packages
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-Informed Neural Networks (PINNs) and hybrid models that respect the physical laws governing the real-world system Applying Deep Reinforcement Learning (DRL) algorithms to optimize processes within simulation
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Simons Collaboration on the Physics of Learning and Neural Computation | California City, California | United States | about 2 months ago
analysis from physics, mathematics, computer science, neuroscience, and statistics to understand how large neural networks learn, compute, scale, reason, and imagine. By studying AI as a complex physical
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, recovery and separation of supernatant, and drying process. Support AI‑enabled data analysis, including collaboration on Artificial Neural Networks and Gaussian Process modelling, to accelerate processing
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units in neural networks, which drive both artificial and natural intelligence. Current projects span a wide range of topics in deep learning theory and theoretical neuroscience. For more information and
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constructs, to study neural network dynamics, disease mechanisms, or drug response using in vitro or ex vivo systems. The candidate will collaborate with neuroscientists, stem cell researchers, and
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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applications of neural networks to the analysis of multi-omic data, models for predicting phenotypes using genotype data, biological data integration, etc.. Participation in these projects will include