204 structural-engineering-"https:"-"https:"-"https:"-"https:"-"FEMTO-ST" Postdoctoral positions in United Kingdom
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responsible for high-throughput engineering of phage genomesand high-throughput phenotyping assays. The successful applicant with work closely with Postdoctoral Research Fellows and they will be part of a
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testing for all three projects. Applicants should hold a relevant PhD/DPhil (or be close to completion) in engineering or a related discipline. Post-qualification research experience and a strong
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to develop a program of work investigating how brains use internal models of task and world structure to enable flexible goal-directed behaviour. The experiments will involve recording and/or manipulating
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research project lead by Oxford Materials (Professors Robert House (PI), Saiful Islam, Peter Bruce), with UCL Chemical Engineering (Dr Rhod Jervis) and 4 industrial partners that brings together expertise in
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the molecular structural dynamics and embedded chiral information in redox-active polycyclic aromatic hydrocarbons influence the emergence of novel photoluminescent properties. While the majority of objectives
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effects while building machine-learning-ready kinetic datasets for predictive catalyst design. You should have a PhD (or about to obtain) in Chemistry or field related to this project (Chemical Engineering
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of researchers to develop new heterogeneous catalysts for conversion of CO2 to light olefins using high-throughput experimental techniques and AI tools. You will have a PhD in chemistry or chemical engineering, a
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Point 25 - 28 £33,482 - £36,130 until confirmation was received. This post is available from 30 March 2026 for an initial period of 12 months. Based within the Faculty of Science, Engineering and Business
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and testing of LLMs. About you You will hold, or be close to completion of a PhD/DPhil in a relevant technical subject (e.g. computer science, statistics, engineering) and possess sufficient specialist
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to work within one of, or across, the four research themes: Learning with Structured & Geometric Models, Low Effective-dimensional Learning Models, Implicit Regularization, and Reinforcement Learning