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Field
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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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for reproducible HPC environments. Experience with CUDA-level optimization or debugging hardware-specific performance differences. Basic knowledge of protein structure, folding, or biophysics. Our University
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or single-cell genomics analysis. Familiarity with graph neural networks, transformers, generative AI or foundation models. Experience working with cloud/HPC environments, workflow orchestration
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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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experience in working with Linux HPCs · Experience in applying machine learning methods to genomics data analysis · Experience in navigating public databases and genomics data repositories
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Experience with HPC workflows (cluster computing, GPU computing, reproducible pipelines) Track record of collaborative work across theory/experiment or interdisciplinary teams All candidates and projects will