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Are you interested in challenging deep learning at its core? And specifically, do you want to perform cutting-edge research and develop novel advances in hyperbolic deep learning for computer vision
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PhD Studentship in Aeronautics: How offshore wind farms and clouds interact: Maximising performance with scientific machine learning (AE0078) Start: Between 1 August 2026 and 1 July 2027
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with machine learning. Self-Driving Laboratories (SDLs) are emerging research environments where experiments are planned, executed, and analyzed in closed-loop workflows that combine automated
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PhD Studentship in Aeronautics: Real-time machine learning and optimisation for extreme weather (AE0073) Start Date: Between 1 August 2026 and 1 July 2027 Introduction: Climate change is
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specifically, do you want to perform cutting-edge research and develop novel advances in hyperbolic deep learning for computer vision? Then check out the vacancy below and apply for a PhD position in this
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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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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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machine learning or trustworthy AI, including experience with robustness assessment and attack/defense mechanisms. Expertise in software security and code analysis, with understanding of common
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training dataset of well-studied volcanoes with known large eruptions, the project will employ statistical and machine learning (ML) methods to identify the strongest predictors of eruption magnitude
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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