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Field
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Highlights The Fregosi laboratory in the Department of Physiology has a unique opportunity for a Postdoctoral Fellow I-III (DOE) to join their team of people working to improve our understanding of the neural
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studying the neural basis of sensory-motor behaviors. Supervision Received The Postdoctoral Fellow will be supervised by Dr. Ana Fernández-Mariño, Ph.D. Examples of Work Performed Perform experiments and
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learning libraries (such as TensorFlow, PyTorch), along with experience in structural modeling tools (e.g., Vienna, Rosetta, RNAstructure) and graph neural networks or transformers applied to molecular
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that has pooled its expertise to tackle fundamental questions in neuroscience – questions such as, "How do neural stem cells choose between self-renewal and differentiation?" "What mechanisms generate
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that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and
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neural processing hardware and design tools, and ML security, and their applications in resource-constrained Embodied-AI systems (like autonomous vehicles, UAVs/UGVs, Robotics, smart houses/cities
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 1 month ago
The ideal candidate would have some, but not necessarily all, knowledge of and skills in: Familiarity with artificial intelligence, machine learning, physics-informed neural networks, or reduced-order
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 1 month ago
Engineering, Systems Engineering, Data Science, or a closely related field Experience with artificial intelligence, machine learning, physics-informed neural networks, or reduced-order modeling (ROM
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decisions. Adheres to University and unit-level policies and procedures and safeguards University assets. Develop various machine learning and data mining models including deep convolutional neural networks
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, a proven publication record, and effective interpersonal skills. Preferred Qualifications: Knowledge of graph neural networks and other geometric deep learning approaches for graph-structured