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Field
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Proficiency in Python and experience working in Linux-based HPC environments or cloud computing platforms Proven experience with deep learning frameworks such as PyTorch, and familiarity with multimodal data
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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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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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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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(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
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annotation, comparative or population genomics, command-line work in a Linux/HPC environment, and scripting in at least one of Python, R, or Bash. Hands-on molecular biology (DNA extraction, library
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, TensorFlow); experience working on HPC clusters is an advantage Familiarity with genomics and regulatory biology (gene expression, transcription-factor binding, variant effects, GWAS/eQTL) is desirable; a
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. Experience comparing simulated and experimental XAS/XAFS spectra. Experience with high-throughput spectroscopy workflows, HPC, synchrotron datasets, or physics-informed AI. **Please include a cover letter that
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tools, especially with application in drug discovery. Experience with high performance computing environment (HPC) / cluster job submission. Knowledge of statistical methods, data science algorithms