98 computer-programmer-"https:"-"U"-"https:" Postdoctoral positions at University of Oxford
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-linear systems of ordinary differential equations and the proficient use statistical programming languages (R, Julia or Python), Bash computing and the development of computational packages are essential
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/test articles), intrusive probes, and optical diagnostics. You’ll plan and run test campaigns, analyse data to advance understanding of material–flow interaction, and disseminate results in seminars
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Applications are invited for a Postdoctoral Research Assciate(s) in the theory of quantum systems. This post is for 2 years. This project will explore theory of quantum computing and simulation
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in tackling many global challenges, from reducing our carbon emissions to developing vaccines during a pandemic. The Department of Computer Science at Oxford is renowned for pioneering research and
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sensing. are essential as well as strong computing skills, including the knowledge of UNIX/Linux, Fortran, Python, or other high-level languages. The post is full time and fixed term for 18 months
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record in a related field, and possess sufficient specialist knowledge in the discipline to work within established research programmes. You will have the ability to identify research objectives and to
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proven expertise in seismic data processing and analysis, knowledge of volcanic/ geothermal processes, strong quantitative skills, and proficiency in Python for scientific computing. You should be
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Postdoctoral Researcher. The group aims to identify, understand, and develop therapies for rare genetic disorders. The group is primarily computational but partners with multiple international labs (including
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Applications are invited for a Postdoctoral Research Assistant in Superconducting Quantum Circuits. This is a fixed-term position until 30 June 2027. We would like the successful candidate to start as soon as possible but must be available to start by 1 April 2026 at the latest. This project...
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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