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candidate will be part of a highly collaborative team and actively interact with Argonne and UChicago scientists as well as external collaborators. Position Requirements Recent or soon-to-be-completed PhD
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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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include: Conducting high-resolution model simulations of convective storms Contributing to model development using AI/ML approaches in areas such as clouds, turbulence, land–atmosphere interactions, and
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solidified artificial intelligence (AI) as a key science accelerator, especially in concert with automated techniques for sample creation, movement, and evaluation. However, the flexibility and intelligence
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successful candidate will conduct research focused on meso- and macroscale mathematical modeling of next-generation batteries, while leveraging advanced artificial intelligence (AI) tools to accelerate
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. We seek a highly motivated candidate to contribute to an interdisciplinary research effort at the intersection of materials science, laboratory automation, and artificial intelligence. This position
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HPCAT staff mentor and interacting on regular basis with HPCAT principal scientist and management. This is a two year term position (with expected impactful progress) and may be considered for extension
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HPCAT staff mentor and interacting on regular basis with HPCAT principal scientist and management. This is a two year term position (with expected impactful progress) and may be considered for extension
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and lanthanides within controlled atmosphere gloveboxes. Apply chemical thermodynamic and kinetic theories to understand processes and develop models of material interactions and behavior in molten salt
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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