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for publication in scientific journals and/or presentations. May also assist in grant writing. Training of neural network models on HDEMG data. Other duties as assigned. Requirements: Doctorate Degree Are you
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and histology datasets. Applying graph neural networks, transformer models and generative AI approaches to study clone-microenvironment interactions. Integrating spatial transcriptomics, single-cell
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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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-processing workflows for single-photon LiDAR and time-resolved imaging, including event-based or neural-network-assisted approaches for efficient extraction of information from sparse photon measurements, low
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multidisciplinary teams. A proactive, curious and resilient approach to research. Desirable Background in brain circuits or body-brain axis. Experience in neural modulation, neuronal tracing, electrophysiology
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on research projects spanning evaluation of deep learning neural networks trained on signed language recognition. The fellow will work closely with the PI Annemarie Kocab and collaborator Alex Lu , Senior
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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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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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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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; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates