PhD Research Project Allocation: September 2023 entry

Application stage

Applicants must review the projects on offer. The projects are listed below with a pdf for further detail. Please provide a ranked list of 3 projects of interest to them: 1 = project of highest interest. Project supervisors will be happy to respond to specific questions you have after reading the proposals. Applicants should contact them by email if they wish beforehand.

PhD Project Allocation Process

Application forms will be reviewed based on the 3 ranked projects specified. Successful applicants will be invited to attend an interview with the Compass admissions tutors and the specific project supervisor. 

Please read instructions in section 'How to Apply to PhD Compass' on our Apply page for information on the application process for 2023/34 entry, where you will find more details regarding the PhD project allocation and the structure of the Research statement. 

PhD Projects for September 2023 entry

Inferring Parameters in Models of the Spatial Genomics of Cancer Tumours - Mark Beaumont and Feng Yu‌  Project: Beaumont Yu (PDF, 189kB) 
Data segmentation for modern datasets - Haeran Cho ‌  Project: Cho (PDF, 377kB)
Statistical learning of quantitative data at scale to redefine biomarker discovery (collaboration with Sciex)- Andrew Dowsey Project: Dowsey (PDF, 895kB)
Wastewater Analysis for Public Health (collaboratin with UKHSA) - Leon Danon Project: Danon (PDF, 91kB)
Bayesian Decision Analysis: a tool for robust climate adaptation decision making under deep uncertainty (collaboration with Met Office)- Laura DawkinsDan BernieDennis Prangle Project: Dawkins_Bernie_Prangle (PDF, 169kB)
Dynamic covariance matrix models for energy forecasting - Matteo Fasiolo and Haeran Cho Project: Fasiolo_Cho (PDF, 170kB)
 HGR maximum correlation - Oliver Johnson Project: Johnson (PDF, 85kB)
 Network meta-analysis of diagnostic test accuracy - Hayley Jones and Nicky Welton Project: Jones_Welton (PDF, 121kB)
Regularised Learning Methods for Inverse Problems and Applications in Imaging Sciences - Rihuan Ke Project: Ke (PDF, 115kB)
Comparing qualitatively different data at scale - Dan Lawson Project: Lawson (PDF, 578kB)
Machine Learning via Statistical Discrepancies - Song Liu Project: Liu (PDF, 120kB)
Scaling up simulation based inference to whole genome data - Dennis Prangle and Mark Beaumont Project: Prangle_Beaumont (PDF, 45kB)
Sparse recovery in group testing - Sid Jaggi Project: Jaggi (PDF, 98kB)

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