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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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symmetries tests and to probe the realm of physics beyond the Standard Model. We aim to appoint at the rank of Assistant Physicist (equivalent to a tenure-track Assistant Professor), with a start date as early
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, and synthesis. Focus Areas (expertise in one or more is highly desirable) Autonomous laboratories for materials synthesis and characterization AI/ML for predictive modeling and inverse design Generative
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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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technologies, and in advancing data-driven risk monitoring approaches for supply chain resilience. The candidate will assist with data collection, analysis, and scenario modeling for a DOE-sponsored assessment
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of Large Language Models (LLMs) for scientific use cases. This position focuses on advancing LLM capabilities to address complex challenges across a range of scientific domains. As part of a
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these areas. Ability to work independently on a day-to-day basis. Demonstrated interpersonal, oral, and written communication. Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and
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The Theory and Modeling Group at the Center for Nanoscale Materials (CNM) seeks an outstanding Assistant Scientist to lead and support frontier research at the intersection of AI/ML, data
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operando experiments under electrical, thermal, or mechanical bias to capture real-time defect dynamics. Integrate multimodal datasets and collaborate with AI/ML teams for data fusion, physics-informed model