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be centralized. The successful candidates will work at the intersection of federated learning, foundation models, multimodal biomedical AI, privacy-preserving machine learning, continuous learning
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Infratsructure Science (DIS) Division. Recently Argonne had successfully developed AI foundation models for weather and Seasonal-to-subseasonal forecasting, hydrology and urban digital twins. As we move
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, computational materials science, chemical engineering, or equivalent experience applying these scientific principles in computational research A strong foundation in quantum mechanics and electronic
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soon-to-be-completed PhD (within the last 0-5 years) in chemistry, physics, chemical engineering, or a closely related field A strong foundation in quantum mechanics and electronic structure theory
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field. Solid foundation and essential knowledge in fundamental electrochemistry (reaction kinetics, mass transport, interfacial phenomena, and thermodynamics). Hands-on experience in electrodeposition
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foundation models. - Apply free energy and enhanced sampling methods to study molecular systems and support model training. - Collaborate with a multidisciplinary team to connect computational findings with
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, including representation-learning and foundation-model approaches, to identify cellular states and predict responses across radiation dose, dose rate, exposure duration, and cell type. Longitudinal
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availability. Relevant Publications: 1. P. Chen et al., Ultrafast photonic micro-systems to manipulate hard X-rays at 300 picoseconds, Nat Commun, 10:1158 (2019). https://doi.org/10.1038/s41467-019-09077-1. 2
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below: https://www.anl.gov/cse/atomic-molecular-and-optical-physics Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in physics, chemistry, materials science, or
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solver, capable of running simulations on the Aurora supercomputer, using AMReX ( https://amrex-codes.github.io/amrex/ ) and the lattice Boltzmann method (LBM). The candidate will develop flow/geometry