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Field
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field Background in formal verification, programming languages, systems, or ML Research experience in one or more of: interactive theorem proving (Rocq, Lean, HOL, Isabelle, or similar), GPU
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computation, communication and storage, optimizing GPU utilization, parallelization, inter-node communication and I/O performance on parallel file systems. You will develop solutions for monitoring
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, GPU architectures, and programming in C++ and Python. This is a 100% TVL E13 position with an application deadline of August 14, 2026. About us Located in the prosperous capital of Bavaria and home
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of the UPSCALE HPC infrastructure; • installation, configuration, administration, and monitoring of: - CPU and GPU computing clusters, - storage systems, - virtualization and cloud computing
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hardware and software stack for supporting cutting edge AI workloads . You will build one of the most capable and automated B2B AI compute stacks in Austria and Europe, running on our own on-premise GPU
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working with AI systems openly and ethically. Its core is a studio of high-performance GPU workstations hosting open-source models locally (image, video, audio and language) where staff and students can
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monitor Slurm-based training and evaluation jobs Debug failures related to distributed execution, checkpointing, filesystem performance, networking, and GPU utilisation Help maintain reproducible
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outstanding performance. Hands-on scripting experience (Bash, Python, or other dynamic language). Unix/Linux programming or system administration experience. Experience with OpenStack and AWS p(EC2/S3) cloud
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working with web APIs (Application Programming Interfaces), high-performance computing cluster, Bash or GPU programming is preferred. When submitting your application, please list your programming
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programming, GPU programming) Astrophysical fluid dynamics and/or magnetohydrodynamics Radiative transfer in astrophysics Machine learning or data analysis for physical sciences Strong programming