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Field
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Information Retrieval. You will also have support from a Senior Company Supervisor. Candidates should have a degree of Master or PhD in Computer Science, Artificial Intelligence, Machine Learning, Software
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interaction, as well as participate in the full range of faculty responsibilities, including scholarly research and service to the University. Applicants should possess a PhD or master degree in
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data and machine learning processing pipelines. It will also focus on developing AI algorithms for semantically enriching data assets with domain knowledge, which feed into building knowledge graphs
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will teach an appropriate mix of undergraduate and postgraduate computing modules within the School of Computing and Engineering commensurate with their technical knowledge and prior experience
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programming), (2) populating agent-based models with realistic agent behaviours (e.g. using machine learning techniques), (3) calibrating large-scale agent-based models and (4) validation and verification
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, Computer Engineering, Electrical and Electronics Engineering programmes. The successful candidate will contribute across the School of Engineering but will play a key role within the Digital Engineering and
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greener transportation and energy. Building on recent advances, the successful candidate will use a powerful combination of dynamical systems theory, optimisation, DNS and machine learning to model and
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Undertake high-quality collaborative research to enhance CNI cyber security using machine learning. Develop and support simulation and benchmark environments for CNI. Design and refine realistic simulations
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programming for data analysis and machine learning/deep learning, using python or R. Ability to work well within a interdisciplinary team. Commitment to contribute to positive research culture. Apply Before: 16
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the expertise of the existing members of the School will be considered. Topics of interest include, but are not limited to: reasoning, autonomous systems, machine learning, AI and industrial processes, machine