Active drifter deployment Drifters are free-floating measurement devices that are released into the ocean. They move with the currents, and provide an alternative source of data to fixed measurement devices (eg anchored buoys). This project is not about techniques to analyse such data. It is about deciding where and when to deploy drifters to get the most useful information. You will devise techniques to design drifter campaigns by combining techniques such as Gaussian process emulation, Bayesian optimisation, and experimental design. You will work with ocean simulators (supplied by collaborators) to develop your techniques, and will have opportunity to collaborate directly with potential users of your methods. The project will build on fundamentals of Bayesian statistics, spatial statistics, experimental design, numerical optimisation, high performance computing, information theory; successful candidates should be already familiar with at least some of these areas. Bandits in real systems Multi-armed bandit theory [https://en.wikipedia.org/wiki/Multi-armed_bandit] is extremely well-studied in situations where there is a very direct link between actions and rewards. However in many situations where we may wish to deploy these techniques, the choice of an action leads to outcomes in a complex and partially-understood way. For example, choosing the price of a finitely-available product for the following day will result in a semi-predictable sales pattern, and consequent amount of stock left at the end of the day. And choosing some hyper-parameters of a learning method for a period of time will result in a semi-predictable performance improvement of the method. This project will develop techniques for such problems, where there is a (semi-)parameterised model of the world, and sequential decisions must be taken to simultaneously learn the model and optimise outcomes. **Currently open advert** PhD Scholarships in Statistical/Machine Learning University of Western Australia, Australia University of Wollongong, Australia University of Lancaster, UK PhD Scholarships are available in the area of statistical/machine learning with applications to metocean and ocean engineering. Research topics in statistics/machine learning identified as PhD projects include (1) sequential decision making and optimal design to augment targeted and efficient data acquisition; (2) data driven spatio-temporal inference/prediction of complex ocean dynamic processes; (3) statistical analyses, emulation, and uncertainty quantification of physical models. The student will be a member of the Australian Research Council’s (ARC) Industrial Transformation Research Hub for Transforming energy Infrastructure through Digital Engineering (TIDE), situated in the Indian Ocean Marine Research Centre (IOMRC) at the University of Western Australia (UWA). TIDE brings together a vibrant international team of researchers in statistics, data science, and ocean engineering. The data science team within the Hub is comprised of researchers located at the University of Western Australia, University of Wollongong, Australia, and University of Lancaster, UK, with expertise in statistics, machine learning, and applied mathematics. Successful applicants will be hosted at one of the aforementioned institutions, depending on research interests and student circumstances, and will engage in collaboration across, and travel between, institutions. A generous scholarship will be made available to fund the student’s studies for three years full-time. An additional top-up scholarship is also available for outstanding candidates. Tuition fees for outstanding international students (for up to 4 years) will be waived. The successful applicant will have the opportunity to work with both Australian and international collaborators, and funding is available for conference travel. Applications are invited from domestic and international students who are able to commence their PhD studies in early 2024. Applicants should hold, or be close to completing, an Honours undergraduate degree or a Masters degree in Statistics, Machine Learning, or a closely related field. The ideal candidate will have an interest in the development of statistical learning/machine learning methodology and computation, excellent mathematical and programming skills, and an interest in using them to model and predict environmental or engineering phenomena. Self-motivation, strong research potential, and good oral and written communication skills are essential criteria. To apply, please send in academic transcripts, a CV, and a cover letter outlining your motivation for conducting research in one of the above areas to Kath Lundy (tide@uwa.edu.au). For informal queries, please contact A/Prof. Andrew Zammit Mangion (azm@uow.edu.au), A/Prof. Edward Cripps (edward.cripps@uwa.edu.au) or Prof. David Leslie (d.leslie@lancaster.ac.uk).

I am a Professor of Statistical Learning, and Director of Engagement, in the Department of Mathematics and Statistics at Lancaster University.  I research statistical learning, decision-making, and game theory.  My research on bandit algorithms is used by many of the world's largest companies to balance exploration and exploitation in real time website optimisation. I led the EPSRC/NERC-funded Data Science of the Natural Environment (DSNE) project at Lancaster University, and was a member of the NG-CDI project, funded by an EPSRC Prosperity Partnership with BT. Prior to my position at Lancaster, I was a senior lecturer in the statistics group of the School of Mathematics, University of Bristol, where I was co-director of the EPSRC-funded cross-disciplinary decision-making research group at the University of Bristol. I was also a partner in the ALADDIN project, a large strategic partnership between BAE Systems and EPSRC, and involving researchers from Imperial College, Southampton, Oxford, Bristol and BAE Systems.

• statistical learning
• decision-making
• game theory, particularly learning in games
• reinforcement learning
• stochastic approximation

I am a member of the Scientific Board of the Smith Institute.

I am currently an external examiner for Edinburgh University and Imperial College London. Previously I was an external examiner at the University of Kent at Canterbury and the University of Leicester.

I have held numerous roles at the Royal Statistical Society.  These include elected Council member, member of the long term strategy group, member of the Academic Affairs Advisory Group, and chair of the Applied Probability Section, and member of the Research Section Committee.

I have served on the programme committee of the London Mathematical Society.

I was a member of the Review into Knowledge Exchange in the Mathematical Sciences (aka the Bond Review) and the subsequent Council for the Mathematical Sciences exploration into the establishment of an Academy of Mathematical Sciences.

I have had the privilege to supervise some great students.  The following have finished and moved on:

• Archie Chapman
• Michalis Smyrnakis
• Harriet Mills
• Ben May
• Steve Perkins
• Kevin Lloyd
• Matthew Arnold
• Simon Smith
• Adnane Ez-zizi
• Beki Floyd
• Hannah Julienne
• James Edwards
• Chris Sherfield
• Simon Smith
• Ciara Pike-Burke
• James Grant
• Henry Moss
• Tom Pinder
• Anja Stein

I am always on the lookout for the next good research student. However, note that it is essentially impossible for me to find financial support for students from outside the EU.