203 structural-engineering-"https:"-"https:"-"https:"-"https:"-"https:"-"LGEF" Postdoctoral positions in United Kingdom
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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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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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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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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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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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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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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
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close to completion) in cognitive neuroscience, psychology, biomedical engineering or a related area ¿ Experience of human neuroscience experiments, including EEG or MEG recording and analysis
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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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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