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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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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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, oral and written communication skills, and ability to interact with people at all levels both within and outside the laboratory. Ability to model Argonne’s core values of impact, safety, respect
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. Integrate domain knowledge from power systems with modern ML methods to create physics-informed, interpretable, and operationally relevant solutions. Build and evaluate models using realistic utility or test
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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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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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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
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modeling of x-ray spectroscopies sensitive to molecular chirality; simulations of x-ray–induced ultrafast electron-transfer, decay, and nuclear dynamics in gas- and liquid-phase systems; and the development
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- Exploring Foundational Models and Agentic AI to address challenges in energy storage and conversion. Position Requirements Candidates must meet the following qualifications: 1. Educational Background: - 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