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after the review date will only be considered if the position has not yet been filled. Position description The Department of Computer Science in the Henry Samueli School of Engineering and Applied Science
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the US and 11th in the world (Research.com). Our department also hosts The Center for Systems Immunology (CSI), which is focused on the analysis and engineering of molecular and cellular networks
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. in Physics, Materials Science, Electrical Engineering, or a related field by the start date. Preferred Qualifications: Hands-on experience in nanofabrication techniques (e.g., electron-beam lithography
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optical sensing techniques. Minimum Qualifications: • Ph.D. in Physics, Materials Science, Electrical Engineering, or a related field by the start date. Preferred Qualifications: • Hands-on experience in
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, innovative, and dynamic research projects. They will learn the routine lab techniques and build strong analytical skills. The work between the two labs is at the interface of biology and engineering, employing
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informatics, biomedical engineering, statistics, or related fields. The lab is engaged in developing novel deep learning and AI-based technologies for digital biopsies from medical images and real-world
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a Ph.D. degree in Aerospace Engineering or a related discipline by the employment start date and demonstrate potential to build an independent research program with externally funded research
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record of scientific productivity is preferable. The successful applicant must have a Ph.D.in Computer Science, Data Science, Statistics, Mathematics, Physics, Engineering or related field at the start of
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 1 month ago
Keeler-Klunk Lab in the Department of Genetic and Cellular Medicine is seeking a highly motivated scientist with expertise in immunology or genome engineering, to join our cutting-edge laboratory
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engineering teams to implement and test models in production environments What We’re Looking For We’re looking for research scientists with a proven track record of applying deep learning to solve complex, high