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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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design spaces. In this role, you will lead a research program centered on AI-driven autonomous synthesis, including: Active learning and Bayesian optimization over synthesis parameters such as
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Argonne’s High Energy Physics Division, the successful candidate will contribute to the design, fabrication, and characterization of superconducting devices based on microstrip-coupled TES technology
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and solid electrolytes. We are seeking candidates who will be able to design experiments and develop methodologies to design material compositions, ink rheological properties, and coating and drying
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The Applied Materials Division at Argonne National Laboratory has an immediate opening for a Postdoctoral Appointee. The candidate will be responsible for reviewing and developing design methods and
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the design, fabrication, and characterization of superconducting devices based on high kinetic inductance materials. This position offers a unique opportunity to help build a new research capability
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, morphology, and device-relevant properties Design active learning, Bayesian optimization, uncertainty-aware modeling, and other adaptive experimental design workflows to guide experiments and