122 postdoc-in-thermal-network-of-the-physical-building Postdoctoral positions at University of Oxford
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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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We are looking for an excellent post-doctoral candidate with a PhD / DPhil (or near completion) in quantum optics, solid state quantum physics, magnetic resonance or related areas. The successful
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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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developing characterisations of network models and interactions with methods in statistical machine learning. The post holder provides guidance to junior members of the research group including project
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at the Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford, OX3 7DQ, and is offered on a full-time basis, fixed-term for 12 months in the first instance. About you You will hold, or be
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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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diversity Our active Psychiatry People and Culture teams and initiatives work to make the Department of Psychiatry as supportive, welcoming and inclusive as possible. Application Process You will be required
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Secretion System. You will build on our recent work in this area (Nature (2018) 564: 77, Nat Microbiol (2021) 6: 221, Nat Microbiol (2024) 9: 1089, bioRxiv (2025)) by carrying out protein biochemical, cell
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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