438 engineering-computation-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"L2CM" positions at Carnegie Mellon University
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, knowledge‑sharing, and supportive collaboration Required Qualifications: Bachelor’s degree in Computer Science, Engineering, Physics, or a related technical field At least 3 years of experience developing
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, faculty members, researchers, and students are revolutionizing focus areas in advanced manufacturing, bioengineering, computational engineering, energy and the environment, product design, and robotics. In
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The Computing Services central IT department provides services that have a strategic impact on university goals. We make service decisions based on interaction and valuable input from colleagues
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Software Engineers to work at the intersection of state of the art computer science and cutting edge astrophysics. The engineers will design and build analysis frameworks that run on the cloud or high
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the disciplines of engineering, design, and business, we can build impactful solutions that create value for real people. Carnegie Mellon University’s Metrics for Technology Products & Services course is looking
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of the following technology areas: hardware/software co-design, performance optimization with heterogeneous and alternative computing systems (CPU/GPU/NPU/etc.), FPGA design, high-performance computing
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collaboration Required Qualifications: Bachelor’s degree or equivalent experience in Computer Science, Engineering, Mathematics, or a related technical field 4+ years of professional experience developing full
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, faculty members, researchers, and students are revolutionizing focus areas in advanced manufacturing, bioengineering, computational engineering, energy and the environment, product design, and robotics. In
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curious to deliver work that matters, your journey starts here! The Department of Engineering and Public Policy (EPP) is a unique department in the College of Engineering at Carnegie Mellon University which
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of the following technology areas: hardware/software co-design, performance optimization with heterogeneous and alternative computing systems (CPU/GPU/NPU/etc.), FPGA design, high-performance computing