347 computer-programmer-"Multiple"-"U"-"Prof"-"Prof"-"U.S"-"O" Postdoctoral positions in United Kingdom
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39 Faculty of Computer Science Startdate: 07.09.2025 | Working hours: 20 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 07.07.2026 Reference no.: 4408 The position
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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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. Ready to be part of our team? Let’s shape the future together! About the team: The Computational Materials Discovery group is looking for a postdoctoral researcher working in the field of machine learning
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services (ECO-WADE).¿ You will plan, organise and lead fieldwork on an upland blanket bog in North Wales, and make measurements of greenhouse gas fluxes, water table depths, and water chemistry. This is part
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of genome organisation and metabolic control—with the bold vision of building synthetic life. In this role, you will develop and apply computational methods to analyse single-cell modalities, focusing on gene
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. Computing time is available on our local cluster and on the Vienna Scientific Cluster (VSC), a supercomputer shared by Austria's major universities. We focus on the development of methods to solve the many
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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