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areas of nanoscience and nanotechnology. Job Title: Junior Postdoc-Electrocatalysis; in situ electron microscopy characterization; deep learning frameworks for TEM image analysis Department: Advanced
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appliances). Explore the integration of large language models and reinforcement learning for real-time optimization, fault self-recovery, and production scheduling in industrial processes. Publish research
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, reinforcement learning, monte-carlo tree-search, causal ML etc. Design, develop, and validate interpretable cross-modal AI/ML models incorporating features from electronic structure theory for predictive
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datasets [e.g. behaviour, simultaneous EEG-fMRI and eye-tracking data]. Main research themes include, but not limited to: reinforcement learning and valuation, risk and uncertainty, confidence and
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reinforcement learning Experience with high-performance computing, physics-based simulations, and multimodal data workflows Demonstrated ability to train and deploy AI/ML models using simulated and experimental
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methods, risk and reliability, stochastic control processes, dynamic programing, deep reinforcement learning. Strong track record in scientific contributions supported by peer-reviewed publications. Strong
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) or discrete manufacturing (e.g., electronics assembly, automotive, home appliances). Explore the integration of large language models and reinforcement learning for real-time optimization, fault self
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human well being mutually reinforce one another. We endeavor to foster an inclusive, collaborative work environment to bring interdisciplinary expertise to solve critical environmental problems
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record, and available funding. Education Requirement: A PhD degree (or about to complete) in machine learning, or a closely related field, is required. Required Qualifications: Practical experience in