63 processor-"https:"-"https:"-"https:"-"UNIVERSITY-OF-MACEDONIA-RESEARCH-COMMITTEE" positions at Harvard University
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Details Title Postdoctoral Fellow in On-Premise Computing for Autonomous Vehicles (Computer Architecture, Machine Learning and Runtime Systems) School Harvard John A. Paulson School of Engineering
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disabilities to perform the essential functions. Prolonged periods of sitting at a desk and working on a computer. Ability to use hands and fingers to operate a computer keyboard, mouse, and other office
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, United States of America [map ] Subject Areas: Computer Engineering / Cloud Computing , Cybersecurity , Software Biomedical Sciences / biochemistry , cancer , development , genetics , genomics , infectious disease , RNA
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, or comparable environment. Strong computer skills, including experience using relational databases, collection management software, and electronic library resources. Experience with digital photography
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travel to New York to support Shop Builds and/or other show-related preparations for up to 2 weeks at a time. Physical Requirements: Ability to sit, type, and work at a computer for extended periods
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are looking for people who are/have: Strong computer and analytical skills including extensive prior experience with Excel, and knowledge of PowerPoint and Word Highly organized and detail oriented with strong
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. Cross-Disciplinary Fellowships (CDF) are for applicants with a Ph.D. from outside the life sciences (e.g. in physics, chemistry, mathematics, engineering or computer sciences), who have not worked in
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of seven years’ post-secondary education or relevant work experience Additional Qualifications and Skills: BS or MS (or equivalent practical experience) in Computer Science, Computer Engineering, Data
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programming and data analysis, are highly encouraged to apply. Additional Information Appointment End Date: This is a one year term position Standard Hours/Schedule: 35 hours per week Visa Sponsorship
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collaborations among operations researchers, statisticians, and computer scientists to overcome the methodological challenges posed by the misalignment between historical methods underpinning modern data science