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laws and symmetries into architectures like neural operators, physics-informed neural networks (PINNs), and graph-based solvers, the project aims to accelerate simulations in areas including protein
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, Neuroscience, or a related field. A strong background in functional neuroimaging with experience in decoding and/or encoding models is required. Candidates with experience with recurrent neural networks will be
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, concentration and functional inequalities • Mathematical aspects of machine learning and deep neural networks • Free Probability aspects of Quantum Information Theory. While excellent candidates with other
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. The appointment is for one year, renewable for a second year given the availability of funds. The focus is on fundamental properties of simulated neural networks, like neural scaling laws, as they manifest in
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/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and
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Free probability theory High-dimensional probability, concentration and functional inequalities Mathematical aspects of machine learning and deep neural networks Free Probability aspects of Quantum
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Nature Careers | Vancouver South Shaughnessy NW Oakridge NE Kerrisdale SE Arbutus Ridge, British Columbia | Canada | about 1 month ago
increasingly utilizes big data, satellite imagery, register data, and advanced methods such as deep learning and neural networks to address major societal challenges related to spatial inequalities and
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-university VLIR-IBOF project entitled ‘MINDFUL: MIcroglia directed NeuroDegeneration FUeled by Lipid metabolism’, prof. Dmitriev and prof. Vergult will put their expertise together to establish neural organoid
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for sequence or structural data (e.g. transformers, graph neural networks) Proved experience in working independently and as part of a multidisciplinary team Evidence of strong communication and scientific
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mechanical engineering, or related field to apply. A strong publication record is encouraged and previous experience in areas such as Neural network vulnerabilities and defenses, Anomaly detection, Adversarial