445 computer-"https:"-"https:"-"https:"-"https:"-"BioData"-"BioData" positions at Carnegie Mellon University
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unprecedented data sets with the potential to answer fundamental questions about the universe. At the same time, the flood of data will introduce new computational challenges. We are looking for full-stack
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wisdom across the SEI AI Division. Requirements: BS in Computer Science or related discipline with eight (8) years of experience; MS in the same fields with five (5) years of experience; PhD in
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program and potential for mentoring and advising undergraduates. All qualified candidates are encouraged to apply. This appointment requires teaching a Computational Linear Algebra for Data Analytics
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Program Executive Offices (PEOs) and Program Management Offices (PMOs), sustainment organizations, contractors, and fleet stakeholders to ensure software solutions meet operational, maintainability, and
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generative AI and large language models, computer vision, multimodal AI, agentic AI, and assurance of AI systems. Additionally, we craft metrics and experimental designs for large-scale cybersecurity research
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About the role The SEI helps advance software engineering principles and practices and serves as a national resource in software engineering, computer security, and process improvement. The SEI
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machine learning methods. Technical Experimentation: You will experiment with modern and emerging machine learning frameworks, methods, and algorithms in application domains that include computer vision
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of International Education, EM Marketing & Communications and Enrollment Systems. Collaborate with strategic campus partners such as Institutional Effectiveness & Planning, Computing Services
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of experience, an MS with 8 years of experience, or a PhD with 5 years of experience in Computer Science, Electrical Engineering, or a related field. You’ve worked in a collaborative team environment as a
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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