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applied research on AI-driven and AI-enhanced industrial energy systems optimization modeling, material flow analysis, and supply chain analysis of industrial commodities and critical materials
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systems. In parallel, they will design and develop agentic AI and physics-aware AI models to accelerate discovery and deepen mechanistic insight in catalysis. This work will be carried out in close
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laboratories for chemistry, materials, biology, etc. AI/ML for predictive modeling and inverse design Generative models, reinforcement learning, and agent-based approaches to streamline experimentation and
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(DUSTIEAIM) observational datasets, together with advanced modeling frameworks such as Energy Exascale Earth System Model (E3SM) or data-driven AI models. Position Requirements Completed or soon-to-be
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laboratory workflows. The position will focus on building the data resources, predictive models, and closed-loop decision frameworks needed to accelerate experimentation and advance next-generation autonomous
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and will be expected to contribute to a safe, collaborative, and results-oriented research environment. The work will emphasize process engineering, experimental systems, and data-driven evaluation
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centralized. The successful candidates will work at the intersection of federated learning, foundation models, multimodal biomedical AI, privacy-preserving machine learning, continuous learning, and agentic AI
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the opportunity for fellows to perform research in a scientifically and technologically rich, mission-driven environment; present and publish research; contribute to the overall research efforts of the Laboratory
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. Proficiency in simulation tools used in analyzing vehicle energy consumption. Passion for and experience in data-driven modeling and analysis. Demonstrated ability to perform vehicle modelling and simulation as
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. In this role, you will lead a research program centered on AI-driven autonomous synthesis, including: Active learning and Bayesian optimization over synthesis parameters such as precursors, temperature