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Field
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knowledge of machine learning with large datasets Experience : Experience with cloud-based, parallel, or distributed computing environments Hands-on experience developing or deploying production ML/AI systems
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-time Hours Per Week 37h15 Offer Starting Date 26 Oct 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a
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community and the general public in the Denver metropolitan area. The current strengths of the faculty are in the areas of algorithms, artificial intelligence, computer networks, graphics, parallel and
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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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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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Systems and contribute research in areas such as distributed systems, cloud and edge computing, high-performance and parallel computing, large-scale data processing, resource management and scheduling
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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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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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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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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