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, computational science, or closely related). Relevant coursework (examples): Computational physics/scientific computing and numerical methods for PDEs High‑performance computing (parallel distributed
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coursework (examples): Computational physics/scientific computing and numerical methods for PDEs High‑performance computing (parallel distributed programming, GPU programming) Astrophysical fluid dynamics
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<span lang="en">TUD Dresden University of Technology</span> | Dresden, Sachsen | Germany | about 23 hours ago
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- compiler frameworks (LLVM, MLIR)
- embedded software development
- computer and accelerator architectures
- parallel and distributed computing
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-tuning large-scale models (LLMs, VLMs) in distributed settings. Familiarity with cluster computing environments (e.g., SLURM) and Linux-based workflows. Prior experience in a research lab, including
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verteilten Systemen (Distributed Settings). Vertrautheit mit Cluster-Computing-Umgebungen (z. B. SLURM) und Linux-basierten Workflows. Bisherige Erfahrung in einem Forschungslabor, einschließlich Beiträgen zu
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training or fine-tuning large-scale models (LLMs, VLMs) in distributed settings. Familiarity with cluster computing environments (e.g., SLURM) and Linux-based workflows. Prior experience in a research lab
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Job Description Do you want to contribute to the green energy transition? Are you excited about developing intelligent power electronic converters and drives with AI and edge computing technologies
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whole fleet can act as one Virtual Power Plant. Read more about the project and other participants here: https://aegis-dn.eu/ Your role: You will be working on “End-to-End Energy Modeling for
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distributed settings. Familiarity with cluster computing environments (e.g., SLURM) and Linux-based workflows. Prior experience in a research lab, including contributions to publications or significant open
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fleet can act as one Virtual Power Plant. Read more about the project and other participants here: https://aegis-dn.eu/ Your role: You will be working on “End-to-End Energy Modeling for Distributed AI