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Field
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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
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high-performance computing (HPC) environments Experience in computational fluid dynamics (CFD) codes and modeling Ability to present and publish results in peer-reviewed journal articles Preferred
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AI, …) and relevant programming frameworks Advanced programming skills in relevant programming languages and contexts (e.g., Python, HPC/GPU programming, big data applications) Strong team spirit and
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relevant programming frameworks Advanced programming skills in relevant programming languages and contexts (e.g., Python, HPC/GPU programming, big data applications) Strong team spirit and experience in
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scripting (R, Python) and programming. Experience with High-Performance Computing (HPC) environments and the management/analysis of large datasets. The ability to communicate effectively in English (both
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of large and multidimensional datasets High Performance Computing (HPC) Low temperature physics High magnetic field physics Synchrotron and neutron facilities X-ray scattering Compensation and Additional
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Python, ML, and AI for chemical applications Preferred: Familiarity with HPC systems Proven track record of research in ML/AI for chemistry Strong coding foundation (Python) Knowledge of C++ and CUDA
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different specific requirements, but we are generally interested in candidates with following merits: Track record of scientific publications; Prior experience with high-performance computing (HPC) systems
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well as command-line bioinformatics tools. Experience with high-performance computing (HPC) environments, including the use of job scheduling systems (e.g. Slurm) – considered an asset. Very good command of English
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(HPC). The postdoc will work closely with visualization researchers, AI scientists, and domain application teams across Argonne and the broader DOE ecosystem. The goal of this postdoctoral position is to