127 postdoc-in-thermal-network-of-the-physical-building Postdoctoral positions at University of Oxford
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at the Department of Materials, Rex Richards Building, South Parks Road, Oxford. There is a possibility that the post may be extended for up to four years if the project’s funding is extended. All applications
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suited to a Postdoctoral Researcher who has established expertise and track record in cardiovascular disease, and could benefit from support of the BHF’s award to develop their career and make major
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navigation algorithms and machine learning models on physical robot platforms. We are particularly interested in candidates with expertise in generative AI and curriculum learning applied to robotics, as
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full time and fixed term for 24 months from 1 October 2025 until 30 September 2027. The postholder will be based at the Centre for Socio-Legal Studies, Manor Road, Manor Road Building, OX1 3UQ, and will
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Building, South Parks Road, Oxford. There is a possibility that the post may be extended for up to four years if the project’s funding is extended. All applications are to be made online using the Oxford
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existing machine learning methods, as well as building robust, well-documented, and reproducible analytics pipelines for long-term use by the wider team. You will carry out data analysis and manage
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advanced fundamental physical understanding of the phenomena at play but accurate predictions in realistic geometries remain difficult. You will be responsible for implementing and validating ice accretion
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BBSRC grant awarded to Prof Francesco Licausi. The work is to be conducted in the Life and Mind Building, Department of Biology, University of Oxford. The postholder will work on the molecular mechanisms
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at: About you Applicants must hold a PhD in Biochemistry, Chemical Biology, Physics, Engineering or a relevant subject area, (or be close to completion) prior to taking up the appointment. You will be
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in continual learning settings. The core focus is on leveraging Reinforcement Learning (RL) to make the training and deployment of LLMs more computationally and sample efficient. This approach aims