75 coding-"https:"-"https:"-"https:"-"Max-Planck-Institut-für-Physik" positions at Oak Ridge National Laboratory
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infrastructure. Participant in an on-call rotation. Infrastructure as Code (IaC) & Tooling Write and maintain infrastructure and deployment code using tools such as ArgoCD (GitOps), Puppet (OS management), Go
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competitors/frameworks (Mojo and JAX) for extreme‑scale heterogeneous systems. The selected researcher will explore how autonomous AI agents and LLM‑driven code generation can co‑evolve with next‑generation
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critically apply design and statistical analysis in ongoing work. Familiarity with medical surveillance, exposure monitoring, and large administrative datasets. Experience developing reproducible analytic code
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understanding of engineering specifications, scope of work, governing codes and standards, requirements for test and inspection, QA, vendor documentation requirements, transportation and ESH&Q to procure nuclear
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to ensure compatibility and safety of system. Inspect electrical systems, equipment, or components to identify hazards, defects, or the need for adjustment or repair and to ensure compliance with codes
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frameworks linking molecular interactions to cellular and network-level behavior (e.g. protein-protein interaction, PPI, network analysis) Optimize simulation codes and workflows for leadership-class HPC
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. Familiarity with medical surveillance, exposure monitoring, and large administrative datasets. Experience developing reproducible analytic code and workflow. Ability to perform scripting in common statistical
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artifacts (journal articles). Almost all of our simulation codes require a mesh to discretize the spatial domain of interest. For some applications, generating this mesh has historically been a labor
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work instructions. Evaluate electrical hardware options and select components that meet project and system‑level requirements, ensuring compliance with applicable electrical codes and standards (e.g
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summaries, figures, and reports/publications as appropriate. Participate in code reviews, model reviews, and data readiness reviews to ensure analytical defensibility, transparency, and fitness-for-use in