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have a strong foundation in artificial intelligence, machine learning, and multi-agent systems, along with experience in programming, data analysis, and model development. Knowledge of interdisciplinary
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of vehicle emissions' impact on air quality using data-driven methods and machine learning. The information gained will be used to determine the required mix of vehicles (i.e. petrol, diesel, hybrid, electric
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main project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods
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supplied by Infineum Ltd. • To incorporate Machine Learning (ML) algorithms into the calculation of the forces on the constituent particles, so as to significantly speed up the algorithm. • To incorporate
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, ‘Preference Elicitation and Inverse Reinforcement Learning’, in Machine Learning and Knowledge Discovery in Databases, D. Gunopulos, T. Hofmann, D. Malerba, and M. Vazirgiannis, Eds., Berlin, Heidelberg
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collaboration with good oral and written communication skills. Previous research experience in machine learning, deep learning and/or computer vision is essential.
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PhD Studentship: Open Radio Access Network (ORAN) for Distributed Edge Computing Orchestration in 6G
experimentation and validation, and machine learning. References of our current/recent work are here: "Automatic Retrieval-Augmented Generation of 6G Network Specifications for Use Cases," IEEE Communications
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to microelectronics and space hardware. The aim is a machine learning approach that can build a model from experimental and operational data, but with sufficient physical insight to ensure that the model is robust and
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felt along the cell population line, resulting in the first-of-its-kind living tuneable sensor with cell-specific response. Unit sensors will be robustly characterised. Data will train a machine learning
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addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods. The student will be