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catalysts used in chemical processes. The successful candidate will combine state-of-the-art quantum chemical modelling alongside machine learning techniques and contribute to the development of predictive
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to quality, integrity, creativity and cooperation. You have a profound knowledge of wireless communications, networking, and signal processing. You have at least intermediate knowledge of machine learning
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collaboration with a leading architectural firm. The candidate is expected to publish in leading Human-Computer Interaction venues. Your competencies You hold a master’s degree in human-computer interaction
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Developing computational models and running computer simulations in science and engineering still requires substantial expert knowledge. Agentic artificial intelligence (AI) offers a great
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-Machine Interfaces (eHMIs) can enable safer and more inclusive interactions. You will: Develop a theoretical framework for identifying key characteristics of AV–VRU interactions and define design criteria
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optimisation tools for aperiodic lattice metamaterials. The research will integrate physics-based modelling with machine learning to enable the efficient exploration of vast design spaces defined by
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strain-rate/high temperature interface contact layer created during LFW of Titanium alloys and the links to key process variables and machine/tooling behaviour. This study will be undertaken using
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models of regulation and dynamics . Flow- and diffusion-based models of cellular dynamics are expressive enough to map any source to any target state, but they fall short of learning the underlying
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. Project Overview The project focuses on developing and applying advanced CFD models for aeroengine oil systems. There will also be opportunities to integrate machine learning techniques for building lower
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AI hardware beyond traditional computing architectures. Gain a unique combination of skills in mathematics, machine learning, and photonics. Be part of a multidisciplinary research team spanning