130 postdoc-in-thermal-network-of-the-physical-building Postdoctoral positions at University of Oxford
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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
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funded by BBSRC and is fixed-term for 6 months. This project is to develop simple cell (SimCell, non-dividing bacteria cell) based biocatalyst to transform waste from cultured meat process into essential
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per week), fixed term to 31 March 2026. Currently located in the Radcliffe Observatory Quarter, Experimental Psychology will be moving to the new, purpose-built Life and Mind Building a
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is opportunity to engage Oxford researchers with common research interests at SoGE and other Departments (e.g. a co-I, Prof Myles Allen, is a staff member of Physics and ECI/SoGE) and Schools (e.g
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atmospheric physics, meteorology, climate, numerical methods, and data science. The Research Associate will be proficient in programming/scripting (e.g., in Python, and/or R, and/or Matlab, and/or Bash script
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to learn new techniques and apply them in an interdisciplinary research environment. Application Process Applications for this vacancy are to be made online and you will be required to upload a supporting
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tomato and pepper as model systems. Work in Oxford will build on our extensive experience in studying bacterial virulence mechanisms and the role of the plant microenvironment in disease development
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model and data biases; (2) build and evaluate XAI tools for external auditing and red-teaming; (3) generate predictive explanations without accessing model internal; (4) providing insight into model