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Field
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of the group. Strong knowledge of performance modeling, simulation, and benchmarking of parallel and distributed computing systems and of the workflow systems that run on them. Familiarity with the FAIR data and
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December 2032. About Us The ML Cloud team designs, runs and safeguards high-performance computing infrastructure tailored to machine learning workloads. Our state of the art systems are distributed across
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and parallelization of scientific simulation codes. In research in theoretical or computational quantum physics. In the development and application of Quantum Monte Carlo methods. Having worked
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development skills; distributed or parallel computing is a plus. Experience designing and executing field experiments in urban or environmental settings, with willingness to engage in fieldwork in dense urban
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, parallel or distributed file systems, cluster management, artificial intelligence tools and techniques, performance engineering, benchmarking and capacity planning. Advanced experience designing and
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functioning in a dynamic technological environment. Preferred Qualifications: Experience with parallel computing, GPU operation (CUDA Toolkit), multi-GPU training, and distributed frameworks for machine
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administration (OpenHPC, Redhat Enterprise 7/8, CentOS 7/8/Stream, Ubuntu), experience with SELinux is a plus Parallel and distributed file system (Lustre) experience is a plus, as well as knowledge of Ceph
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will play a key role in building a parallelized, agent-driven exploration system and integrating a multimodal detection pipeline, ensuring real-time performance, scalability, and deployment readiness in
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dependencies for execution on Alps / CSCS infrastructure. Run and monitor Slurm-based training and evaluation jobs. Debug failures related to distributed execution, checkpointing, filesystem performance
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? No Offer Description The researcher’s scientific activity will be carried out within one or more of the following areas: Computer Architectures; Databases; Parallel Computing and High-Performance