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Field
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vision systems, on the consideration of strong constraints on processing times and on the use of machine learning techniques in specific contexts (e.g. embedded targets, little data or explainable AI
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approaches that combine artificial intelligence, machine learning, natural language processing, and social sciences. This collaborative and cross-sectoral approach aims to produce robust methods for evaluating
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This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites Research Groups at the Faculty of Engineering, which conduct cutting-edge research
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-flexible technologies, and integrate machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their
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research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet
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knowledge of wireless communications, and signal processing. You have at least intermediary knowledge of machine learning algorithms, including federated learning, split learning, and graph neural networks
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bio materials and porous materials PhD student candidate 2 with background in computer science, AI, machine learning or related fields with the experience in CFD, ANSYS, COMSOL The successful candidates
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drug design” is led by Docent Juri Timonen, at the Division of Pharmaceutical Chemistry and Technology. Our aim is to create new machine learning and artificial intelligence methods to accelerate drug
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operational data and machine learning. You will be based at UCL mechanical Engineering, and collaborate with industry and port partners on system design, prototyping, and lab-based trials. Key responsibilities