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Field
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, cognitive neuroscience, or related fields. Essential qualifications include: Interest in neural mechanisms of experience-dependent behavioral change Readiness to contribute to task development (e.g., scanner
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of the position is method development. We are seeking a postdoctoral researcher to develop next-generation AI models of cellular dynamics, with a focus on scalable neural network architectures that represent
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predictive maintenance in chemical plants. Key Responsibilities: Create and implement hybrid AI models that merge machine learning techniques with mechanistic frameworks (like physics-informed neural networks
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with traffic jams spreading like oil spills over entire networks. We believe traffic management based on reliable predictions is therefore crucial to ensure accessibility and safety, especially during
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models and agents: how to consolidate and transfer physical knowledge into learning systems for the real world. Physics engines and physics-informed neural networks are powerful, but the open problems
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complex dynamic system. Minor disruptions can lead to major delays with traffic jams spreading like oil spills over entire networks. We believe traffic management based on reliable predictions is therefore
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | 21 days 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 | 21 days 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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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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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view