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in the school. Applicants should hold a PhD or equivalent qualification in Statistics or in a closely related Data Science area such as Biostatistics or Statistical/Machine Learning, and have a track
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/machining processes, artificial intelligence/machine learning/data science in the manufacturing environment. The successful candidate should be recognised internationally for their research and teaching
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, motivation to learn new skills as well as excellent written and oral skills is essential.Example publications from the Lab:•Detection of host cell microprotein impurities in antibody drug products. 2024 Nature
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critical component analysis, and (iii) development of Automation of ML model and data selection. The applicants should have knowledge of machine learning and optical networks and willing to engage in testbed
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software testing and Machine Learning components. This research will have real-world impact through close collaboration with industry partner and offers the opportunity to contribute to cutting-edge
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Professor / Full Professor of Advanced Manufacturing, School of Mechanical and Materials Engineering
/machining processes, artificial intelligence/machine learning/data science in the manufacturing environment. The successful candidate should be recognised internationally for their research and teaching
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-functionally with different stakeholders, knowledge sharing, and building relationships. This role requires excellent communication and interpersonal skills, computer literacy, organization and an awareness
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involves careful review and systematic labelling of large volumes of rugby footage to support Machine Learning applications and performance analysis. This hands-on role is particularly suited to applicants
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datacentres, over 7000 computer workstations, 300+ websites, research infrastructure and a customer base of more than 30,000 users. In addition, it provides and supports the University’s business systems and
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and cognitive trajectories with the aim of helping to lower the age of diagnoses. Machine learning techniques will be applied to combine datasets to establish the best predictive models for children’s