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deep neural networks to guide the development of algorithmic paradigms aimed at combining statistical optimality with computational efficiency. Reinforcement Learning through Stochastic Control. We will
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Job Description The postdoctoral researcher will conduct independent and collaborative research on projects involving brain network analysis, including investigations on the Drosophila connectome
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), ideally geometric/graph neural networks, equivariant models, or generative models. Interest in applying AI to molecular or biological problems; prior structural biology experience is a plus but not required
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-learning architectures for sequential data (e.g., Transformers, graph neural networks, state-space models). Experience with OpenCV, GPU-accelerated inference, Docker, and modern software engineering
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with the goal of improving human health. Aligned with Rutgers University–New Brunswick and collaborating university wide, RBHS includes eight schools, a behavioral health network, and five centers and
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and qualifications Expertise in advanced machine learning, deep learning and image vision techniques with focus on EO data (e.g. deep convolutional neural networks, transformers, deep learning based
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-level approach, from genes and molecules to neural networks and psychosocial systems”, PN-IV-P6-6.1-CoEx-2024-0139 Where to apply E-mail [email protected] Requirements Research FieldPsychological
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University of California, Los Angeles | Los Angeles, California | United States | about 12 hours ago
forests, gradient-boosting methods, PLS-based approaches, neural networks, or related methods is preferred. Experience with feature selection, model interpretation, cross-validation, external validation
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on developing novel neural network approaches to predict protein conformational dynamics from fixed protein structures, addressing fundamental challenges in structural biology with broad applications in
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-learning models (e.g., graph neural networks, equivariant architectures) in collaboration with computer science researchers. Applying developed models to problems in Earth and planetary interiors, such as