87 computer-programmer-"Multiple"-"Prof"-"O.P"-"U.S" "GEORGETOWN UNIVERSITY" Postdoctoral positions at University of Oxford
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, to co-ordinate multiple aspects of work to meet deadlines. You will undertake laboratory work as required, such as sample preparation, cell culture, analysis of tumour samples and, tissue staining. Other
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Mobility Reading Group led by Nobuko Yoshida. The successful candidate will be located in the Department of Computer Science Reporting to Professor Nobuko Yoshida, the post holder will be responsible
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We are seeking five full-time Postdoctoral Research Assistants to join the Computational Health Informatics Lab at the Department of Engineering Science, based at the Institute of Biomedical
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full-stack approach to suppressing errors in quantum hardware. This research focuses on achieving practical quantum computation by integrating techniques ranging from hardware-level noise suppression
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with relevant experience. Along with possessing sufficient knowledge in the discipline to work within established research programmes, having had previous experience of contributing to publications/presentations
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engineering, computer science or other field relevant to the proposed area of research. You should have a good track record of robotic publications/presentations in the field of healthcare, possess sufficient
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research initiative funded by ARIA, titled Aggregating Safety Preferences for AI Systems: A Social Choice Approach. The project operates at the interface of AI safety and computational social choice, and
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Contract type: Fixed term for 2 years in the first instancewith the possibility to extend for a further 3 Hours: Full-time About the role We are seeking a highly motivated and ambitious Postdoctoral Researcher to join the Translation Biology Research Group led by Mr Alex Gordon-Weeks and...
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will have or be close to the completion of a PhD in Neuroscience, Psychology or a closely related discipline. With in-depth knowledge of cognitive and computational neuroscience including motivation
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with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly