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annum inclusive of Oxford University weighting Potential to under fill at grade 06RS: £34,982-£40,855 per annum inclusive of Oxford University weighting The Department of Computer Science seeks to employ
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 22 hours ago
candidates are being sought to join a basic and translational research program focused on the immunology of T cell-mediated autoimmune disease; specifically type 1 diabetes (T1D). Our group is part of
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Computer Science, Optical Communication Engineering, or related field. Candidates at the PDRA level must have a PhD Degree in Computer Science, Optical Communication Engineering, or related topics. Applicants should
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Experience: Candidate must hold a PhD or equivalent degree in neuroscience, biochemistry, chemistry, molecular biology, psychology, computer science, biomedical engineering, biology, or other relevant
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as well as the stepwise process of leukemogenic transformation. Our approach integrates gene editing in human and murine primary hematopoietic and iPSC-derived stem cells, in vivo zebrafish and mouse
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APPLICATION INSTRUCTIONS: CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply
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We are offering an exciting opportunity for a Postdoctoral Fellow/Research Associate to assist with multiple areas of the low-carbon materials research programme. In this project, you will assist
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: A PhD in Computer Science, Engineering, Mathematics, theoretical Physics or other degree programs from top universities involving at least one of the following topics: Machine Learning, AI, Dynamic
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science, or specific skills to a team delivering a project or program. Through this work, you will build scientific independence, develop new science and leadership skills, and establish a growing
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About the Role The project “An Erlangen Programme for AI” (funded by the UKRI), will broadly involve applying advanced mathematical techniques for understanding training in neural networks, with