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enable physics-informed AI agents that accelerate discovery in catalysis science. The successful candidate will focus on the development and application of machine learning and AI approaches to analyze
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. The successful candidate will be a key contributor to a multidisciplinary co-design team spanning material science, computing, and electronic engineering, with the goal of enabling next-generation detector
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computational models for industrial capacity planning, logistics optimization, material flow analysis, and supply chain analysis. Apply artificial intelligence, machine learning, LLMs, and advanced statistical
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, and networked microgrids. Design and implement reinforcement learning, game-theoretic, or robust decision-making approaches for adaptive control of DER and microgrids under uncertainty and adversarial
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is available for the design, licensing and operation of MSRs and other energy systems. Develop and perform laboratory tests and electrochemical operations with molten salts that may contain actinides
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and supercritical conditions. The ability to assemble new batch and flow processes is a plus. Furthermore, we will teach the candidate about engineering design of chemical processes; however
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to effective therapeutic strategies targeting IDPs Collaborate on the development of open-source machine learning tools to support these therapeutic designs Work closely with high-throughput screening teams
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Argonne National Laboratory is seeking a highly skilled Postdoctoral Researcher to work in the Separations and Bioprocessing Group in the Applied Materials Division (AMD) on developing, designing
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-of-the-art data management, machine learning and statistics techniques. With the advancement of Exascale systems and the variety of novel AI hardware designed to accelerate both training and inference
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, and evaluation in distributed and privacy-aware settings. While the position is supported by an AI for Science project on privacy-preserving federated learning, the broader objective is to advance