201 structural-engineering-"https:"-"https:"-"https:"-"https:"-"LGEF" Postdoctoral positions in United Kingdom
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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
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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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agenda for sustainable livestock in Europe. You will also help design and facilitate structured stakeholder/scientific workshops to review and refine these findings. You will be responsible for integrating
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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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Experience in participating in research projects Didactic skills / experience in e-learning IT skills Knowledge of university processes and structures is desirable Excellent knowledge of English (C1) GFL
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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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administrative tasks in research, teaching and administration. This is part of your personality: Completed doctoral/PhD studies in business administration or related topics (e.g. industrial engineering
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Computer Science, Chemistry, Chemical Engineering, Physics, or Materials Science. You will develop optimisation and machine-learning algorithms for human- and literature-informed discovery of new materials