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(CO2) conversion processes and contribute to engineering design of upscaled processes. The candidate will be a part of the Applied Materials Division (AMD) within AET at Argonne and will contribute
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. The project will involve development of novel parallel algorithms to facilitate in-situ analyses at-scale for multi-million and multi-billion atom simulations. In this role, you can expect to work on enhancing
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
mathematics, or a related field Candidates should have expertise in two or more of the following areas: Uncertainty quantification, numerical solutions of differential equations, and stochastic processes
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). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative AI methods as
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range of molecular systems, including: Transition-metal complexes (e.g., chiral ruthenium and iridium complexes) Local and nonlocal inner-shell decay processes in solvated ions and transition-metal
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develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel solver, capable of running
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(HPC): Experience with parallel computing (MPI, OpenMP, CUDA/HIP) or running workflows on supercomputing clusters. Software Engineering: Knowledge of version control (Git), containerization (Docker
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of advanced bioleaching processes for recovery of critical minerals from secondary materials The candidate will design and execute experiments; operate, monitor, and troubleshoot bench- and pilot-scale
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The Applied Materials Division, Process R&D and Scale up Group at Argonne National Laboratory is seeking a Postdoctoral candidate to conduct general research in material science and electrochemistry
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. The researcher will develop and apply physical, chemical, and electrochemical models for advanced battery technologies and associated manufacturing processes. This work will quantify and explain relationships