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neuroscientists pursuing advanced research and development in neuromorphic computing, artificial intelligence, and spiking neural networks across a range of applications. A strong background in theory (e.g
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for tokamak fusion reactors. Specific responsibilities include: Neural Surrogate Modeling: Develop, train, and validate fast neural-network surrogate models (e.g., transport surrogates, edge surrogates, free
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fundamental research in physics-informed and symmetry-aware machine learning for nonadiabatic excited-state molecular dynamics. Develop and evaluate equivariant graph neural networks and related architectures
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Carilion (VTC). About the Lab: The lab integrates state-of-the-art neural recording technologies, complex cognitive tasks, and computational models to investigate the neural basis of flexible cognition. From
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. Key Responsibilities Research & Development: Integrate Physics-Informed Neural Networks or Reinforcement Learning to create realistic human movement and interactive social behaviors within XR
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highly collaborative research team dedicated to advancing our understanding of epilepsy, sleep physiology, and neural networks through cutting-edge clinical and translational research. Working alongside
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frameworks. L–H/H–L transition physics, pedestal evolution, or core-edge coupling. Analysis and validation using experimental data from tokamak facilities. Machine learning, scientific AI, neural-network
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such as transformers, self-supervised learning, multimodal learning, generative models, graph neural networks, or foundation models. Experience with structural and/or functional brain modeling. Familiarity
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Qualifications Experience with graph neural networks, machine-learning interatomic potentials, or related scientific machine-learning methods for atomistic systems. Familiarity with uncertainty quantification
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human health. Aligned with Rutgers University–New Brunswick and collaborating university wide, RBHS includes eight schools, a behavioral health network, and five centers and institutes that focus