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and technologies, and in advancing data-driven risk monitoring approaches for supply chain resilience. The candidate will conduct comprehensive supply chain mapping, modeling, and analysis—integrating
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to quantify energy consumption, performance and cost benefits. In this role, a successful candidate will perform vehicle modelling and simulation of advanced powertrains to quantify the impacts of new component
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and integrate emerging AI techniques (e.g., agentic workflows, LLMs) into scientific problems, ensuring methods effectively solve real domain challenges. Advanced Model Development: Design and debug
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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors
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agents for X-ray spectroscopy by integrating large language models (LLMs) with physics-aware spectroscopy workflows. The researcher will work closely with a multidisciplinary team of X-ray physicists and
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. Cosmological research within CPAC covers theory, modeling, observations, and experiments targeted at dark energy, dark matter, primordial fluctuations, inflation, and neutrinos. Theory and modeling activities
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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Argonne National Laboratory seeks a postdoctoral researcher to help build a high-resolution coastal-urban flooding modeling capability within the Energy Exascale Earth System Model (E3SM
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and novel device technologies Develop, validate, and maintain simulation and modeling frameworks for detector performance, characterization, and benchmarking Analyze simulation results and experimental
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advanced computing, optimization, and data analytics technologies. The postdoctoral researcher will work with a team of researchers on solving challenging problems using optimization, stochastic models