About the role
This role offers an opportunity to contribute to the School’s established strengths in applied statistics, artificial intelligence, machine learning and data science. You will drive forward research at the interface of mathematics, statistics and computer science, developing new methodologies for interpreting complex data and strengthening the theoretical foundations of modern AI and data driven approaches. The role provides excellent potential for interdisciplinary collaboration, including engagement with partners in health and life sciences, space research and environmental science, and you will be expected to publish high quality research, present at conferences and apply for external funding to support your work.
You will also contribute to a broad range of undergraduate and postgraduate programmes, delivering research informed teaching in applied statistics, data science and machine learning. You will support students through lectures, tutorials and project supervision, and play an active role in designing, enhancing and delivering modules that equip graduates with strong analytical and computational skills. The role involves contributing to assessment activities, quality assurance processes and the wider academic life of the School, helping to ensure an excellent student experience across all levels.
About you
You will hold a relevant PhD and post doctoral research experience in applied statistics, artificial intelligence, machine learning or data science, along with a strong record of high quality publications. You will be able to teach specialist topics in these areas at both undergraduate and master’s level, and you will communicate complex ideas clearly and effectively.
You will be an organised and collaborative academic with strong interpersonal skills and the ability to manage your research, teaching and administrative responsibilities effectively. You will have a solution focused approach, a willingness to contribute to academic leadership when required and the ability to work with colleagues across disciplines.