20 learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" positions at Argonne
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desirable): Autonomous laboratories for chemistry, materials, biology, etc. AI/ML for predictive modeling and inverse design Generative models, reinforcement learning, and agent-based approaches to
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desirable): Autonomous laboratories for chemistry, materials, biology, etc. AI/ML for predictive modeling and inverse design Generative models, reinforcement learning, and agent-based approaches to
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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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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
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storage. ESRA is a leader in leveraging artificial intelligence, machine learning, and autonomous labs to accelerate the pace of research. The Director will provide strategic and operational leadership
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The Argonne team is seeking two highly motivated postdoctoral researchers to help shape the next generation of secure, scalable, and continuously learning AI systems for biomedical discovery. This
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ideal for someone who enjoys working at the intersection of data science, machine learning, materials research, and experiment, and who is motivated to translate computational advances into real
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applying artificial intelligence (AI) and machine learning (ML) methods for the autonomous, self-driving synthesis of nanoscale and quantum materials. This is an exciting opportunity to help shape a
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Argonne National Laboratory to advance learning-enabled imaging methods. This position offers a unique opportunity for candidates with backgrounds in electrical engineering, computer science, applied
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catalyst design. This role involves conducting multiscale modeling, spectroscopy simulations, and the development of machine learning methods and automated workflows for multi-fidelity, multiscale