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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 18-Mar-26 Location: Boston, Massachusetts Type: Full-time Categories: Academic/Faculty Computer/Information Sciences
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skills and knowledge in the discipline areas of Cyber Security and/or Cloud Computing. You bring current industry expertise, such as network security frameworks (e.g., CCNA Network Security) or cloud
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experience in computational biology or cancer genomics Experience with high-performance or cloud computing (e.g., HPC, AWS, GCP) At least one first-author peer-reviewed publication Strong communication and
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to quality, integrity, creativity and cooperation. You have strong interest in communication networks, distributed systems, or cloud/edge computing. You have at least basic programming skills (e.g., Python, C
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for our multidisciplinary research focusing on transportation safety and mobility (http://umtri.umich.edu ). Established in 1965, we maintain the highest standards of excellence as we conduct research
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learning, administration, and healthcare. UIT is embarking on a long-term strategy to plan, replace, and implement new cloud-based solutions for several enterprise-wide systems. UIT seeks an experienced
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involves deep collaboration with research computing, security, network, infrastructure, and operations teams while continuously evaluating cloud, on‑premises, and hybrid environments. The architect plays a
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(Senior Enterprise Engineer - Data Management Team) proactively manages, maintains, and influences UI Health's Enterprise computing environment which includes on premises and cloud computing systems and
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University of California, San Francisco | San Francisco, California | United States | about 9 hours ago
implementing next-generation platform solutions such as PaaS, cloud computing services, and serverless computing solutions that enable AI or machine learning. Extensive experience working in a project-based
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, visualisation of results, and automation of computational experiments; optimising code performance and supporting the integration of models with research infrastructure (e.g. HPC, cloud, MLOps pipeline