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College Station, Texas Job Type Staff Job Description Our Commitment Texas A&M University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives
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Research Back Profile Areas Cluster of Excellence CMFI Cluster of Excellence GreenRobust Cluster of Excellence HUMAN ORIGINS Cluster of Excellence iFIT Cluster of Excellence Machine Learning Cluster
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: 273298500 DePaul University Post Doctoral Researcher NSF SCHOLAR Loop Campus (On-site) Job Description The Highlights: The Medical Informatics Lab at DePaul University's School of Computing is seeking a
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, California 95616, United States of America [map ] Subject Areas: Physics / Astronomy , Astrophysics Computational , Machine Learning Salary Range: Starting at $71,491/year or higher depending on experience
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operation · Application of artificial intelligence or machine learning in energy or engineering systems 5. Strong programming and modelling skills using relevant tools such as Python, MATLAB
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) years of related work experience. Experience developing machine learning methods. Experience writing, submitting, and editing peer reviewed publications. Expertise in computer science applications
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
or high-performance computing environments. - Strong background in statistical modeling and machine learning, especially applied to spatiotemporal data. - Familiarity with urban systems, urban emissions
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. Additional background in renewable energy, surface science, catalysis, and/or machine learning. Strong programming skills in Python and some exposure to machine learning. Ph.D. in Materials Science, Physics
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, Robotics, Computer Vision, or related disciplines. Proven expertise and hands-on experience in one or more of the following areas: large language models (LLMs), end-to-end learning, AV localization
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artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic