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contribute significantly to these growing fields. This PhD position is ideal for candidates interested in the following areas of machine learning: Geometric learning: exploiting the structure of data (e.g
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approaches to couple machine learning potentials with molecular mechanics force fields, and they will investigate the use of machine learning potentials as data sources for training force fields. They will
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on Machine Learning and Psychophysiological Deception Detection. The studentship is part sponsored by GCHQ and funded for up to 3.5 years with fees and a stipend at the standard UKRI rate. The position is only
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develop methodologies (such as acoustic emission method) detecting early signs of damage, leaks, or degradation before they become critical. We will also leverage the latest developments in machine learning
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approach that integrates machine learning algorithms, blockchain technology, and IoT devices with digital twin systems. The scientific objectives of the project are as follows: Objective 1: Investigate how
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for Real-World Optimisation and AI Applications Brain-Computer Interfaces & their Applications Computational Neuroscience: Reinforcement Learning and Microzones in the Cerebellum Explainable Generative
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Start Date: Between 1 August 2026 and 1 July 2027 Introduction: This project fuses machine learning (ML) based inverse design approaches and topology optimisation (TO) to realise multiscale
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Subject area: Drug Discovery, Sustainability, Laboratory Automation, Microfluidics, Machine Learning Overview: This highly interdisciplinary 4-year funded PhD studentship will contribute to cutting
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the state-of-the-art wind tunnel facilities of the Department of Aeronautics, and will utilize novel theoretical and machine learning tools. You can expect to become an expert in aerodynamics and turbulent
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About Us The applicant will join the new Wellcome-funded Imaging Machine learning And Genetics in Neurodevelopment (IMAGINE) lab, in the Research Department of Biomedical Computing. The post will