499 computer-science-intern "https:" "https:" "https:" "https:" "UCL" "UCL" positions at Carnegie Mellon University
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, we want to hear from you! Requirements: BS in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with eight (8) years of experience or equivalent
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; ensuring project deadlines are met. Engineering mechanical and system components for robot experimental setups. Maintaining administrative records and tracking and adhering to established budgets. Collecting
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What We Do At the SEI Artificial Intelligence Division, we conduct research in applied artificial intelligence and the engineering questions related to the practical design and implementation of AI
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Electrical Engineering, Computer Science, Statistics, or related discipline with 3 years of experience in hands-on software development; OR MS in the same fields with 1 year of experience; OR PhD in a relevant
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Carnegie Mellon University is a private, global research university that stands among the world’s most renowned education institutions. With ground-breaking brain science, path-breaking performances
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national resource in software engineering and computer security. SEI works closely with academia, defense and government organizations, and industry to continually improve software-intensive systems. Our
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collaboration pathways and keep the lab connected to emerging practice. Requirements Education / Experience BS in Computer Science, Electrical Engineering, Statistics, or related field with 10 years of relevant
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will vary depending on job function. Functional areas could include audio, piano, print shop, etc. Other duties as assigned. Flexibility, excellence, and passion are vital qualities within Computing
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divisions including but not limited to the provost division, academic colleges, enrollment management, student success, finance and budget, student affairs, human resources, and computing services. They will
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models such as GPT and LLaMA, designing and deploying agentic workflows, as well as apply and advance traditional ML research and engineering across domains such as natural language processing, computer