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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 28 days ago
management, or storage integration * Databases, data platforms, distributed systems, GPU-enabled computing, or MLOps workflows * Experience providing technical leadership, mentoring developers, or owning major
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: Psychiatry Group or Departmental Website: https://stai.stanford.edu(link is external) https://neuroailab.stanford.edu/(link is external) https://neuroscience.stanford.edu/(link is external) Does this position
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Research Center for Molecular Medicine (CeMM), ÖAW | Vienna, Virginia | United States | 3 months ago
, operate, and scale HPC clusters and GPU resources to support computational biology, genomics, and machine learning workloads. Manage virtualization and containerization platforms (VMware, KVM, Docker
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offered a salary at or near the top of the range for a position. Link to benefits. https://jobs.ornl.gov/content/Benefits/?locale=en_US Overview: We are seeking a Machine Learning Research Scientist who
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scientific computing ecosystems and/or libraries (e.g. HPC clusters, cheminformatics tools such as RDKit). Competency in CUDA or other GPU APIs is highly desired but not required. • Familiarity with laboratory
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datasets, high-end GPU and storage infrastructure, international research collaborations, and dedicated funding for international conference participation. Supervision The PhD candidate will be supervised by
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Processing Unit (GPU) hardware. Working Conditions Needs to be able to successfully perform all required duties. Office/research environment; some travel and weekend work is required. UTRGV is a distributed
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the NEDO project: "Project to Develop a Multimodal Foundation Model for AI Robots and Physical AI." If selected through this open call, you will be obligated to work exclusively on this NEDO project. https
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with thousands of CPUs and GPUs, petabytes of storage, and high-speed, low-latency networks across multiple data centres in Switzerland. As research workloads continue to evolve, we are building a hybrid
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working on the LHCb experiment at the Large Hadron Collider. The successful candidate will focus on developing LHCb’s real-time data analysis systems, including the GPU-based software trigger. This work