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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-aware refinement strategies that go
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films, including microwave resonators for frequency-domain multiplexed readout of single-photon detectors using kinetic inductance current sensors Develop and characterize superconducting nanowire single
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(typically completed within the last 0-5 years) in Chemistry, materials science or related field. Knowledge and hands-on experience in the following areas: organic chemistry and synthesis. Experience in flow
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learning (ML) to address future physics and detector challenges. Current physics interests include Standard Model measurements and searches for new phenomena. We welcome applicants who are excited
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, characterizing mass transfer and selectivity under flow conditions, and screening and tuning DES compositions to optimize solubility, speciation, and electrochemical accessibility for target elements
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models in Argonne’s GREET model and related analytical tools. The appointee will evaluate current and emerging production pathways, including lithium production from brines and hard-rock resources, lithium
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experimental results and contribute to high-impact scientific publications and conference presentations. 5. Demonstrated interest in, and commitment to staying current with, advances in quantum materials
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applied research on AI-driven and AI-enhanced industrial energy systems optimization modeling, material flow analysis, and supply chain analysis of industrial commodities and critical materials
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the supervision of more experienced researchers. Current project opportunities include quantifying the impact of advanced vehicle technologies applicable to light duty, medium duty and heavy-duty vehicles, hybrid
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current sensors) Develop and characterize superconducting nanowire single-photon detectors (SNSPDs) using high kinetic inductance materials such as NbN, TiN, and NbTiN, targeting high detection efficiency