Statistical modelling for single-cell RNA sequencing data for crucial health applications - PhD (Funded) Ref: 5486

Supervisors

Dr Magdalena Strauss - University of Exeter

Dr Akshay Bhinge - University of Exeter

Prof Marc Goodfellow - University of Exeter

The University of Exeter’s Department of Mathematics and Statistics is inviting applications for a PhD studentship fully funded by the faculty of Environment, Science and Economy to commence on 22 September 2025. For eligible students the studentship will cover Home fees plus an annual tax-free stipend of at least £ 20,776 for 3.5 years full-time.  The student would be based at the Living Systems Institute at the Streatham Campus in Exeter.

Statistical modelling for single-cell RNA sequencing data for crucial health applications

Supervisors: Magdalena Strauss (Mathematics and Statistics), Akshay Bhinge (Clinical and Biomedical Sciences), Marc Goodfellow (Mathematics and Statistics)

Single-cell RNA sequencing can quantify the activity of each human gene in individual cells. This has allowed much better insight into heterogeneity across different cells, and helped identify potential drug targets in precision medicine. This fully funded PhD project applies principled statistical modelling to help identify causes of disease and potential drug targets, and to understand mechanisms behind resistance to drugs. 

Application 1: identifying potential therapeutic targets for ALS

ALS is a fatal neurodegenerative condition characterised by a loss of motor neurons that leads to progressive paralysis and death usually within 3-5 years post-diagnosis. The analysis performed as part of this PhD project will aim to identify key genes as potential therapeutic targets, i.e. genes that will lead to a desired therapeutic outcome if targeted by a drug.

Application 2:  identifying mechanisms of drug resistance in cancer

When cells divide, mistakes lead to DNA mutations. DNA mutations are associated with many diseases. Precision genome editing allows the re-creation of mutations at large scale in a lab. The student will investigate the mechanism with which mutations cause drug resistance in cancer, in collaboration with the Coelho lab at the Wellcome Sanger Institute, who will be performing the experimental work.

Application 3:  identifying causes of congenital heart defects

Congenital heart defects (CHDs) are the most common birth defect, affecting ~1% of live births. Despite advances in medical and surgical interventions, they are a leading cause of foetal death and infant mortality. Despite this over half of all CHD cases have no definitive cause. In collaboration with the Tyser lab (University of Cambridge) the student will explore the relationship between phenotype and genotype using CHD models to define mechanisms which could underpin disease.

Statistical challenges

The applications described above have a complicated multi-level structure. Data include several patients or replicates, several types of cells, several different genetic backgrounds, or groups of cells edited using different reagents. Accurate modelling of structures and dependencies in the data is essential to control the rate of false positives concerning the identification of potential therapeutic targets. Further statistical areas relevant to this project include high-dimensional statistics and uncertainty quantification. 

Candidate suitability

This PhD studentship is a great opportunity for a student interested in applying statistics in a collaborative and inter-disciplinary setting, with impactful applications in medical research. The project will suit students with a strong background in an appropriate quantitative subject such as mathematics, statistics, machine learning, computer science, bioinformatics, physics or econometrics, and an enthusiasm for medical applications. Applicants for this studentship must have obtained a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK. Preferably, applicants should have or be about to obtain, an MSc degree, or the equivalent qualifications gained outside the UK. Experience in coding in Python or R is essential.

For eligible students the studentship will cover Home fees plus an annual tax-free stipend of at least £ 20,776 for 3.5 years full-time. 

The studentship will be awarded on the basis of merit. Students who pay international tuition fees are eligible to apply, but should note that the award will only provide payment for part of the international tuition fee (~£24k) and no stipend.   

International applicants need to be aware that they will have to cover the cost of their student visa, healthcare surcharge and other costs of moving to the UK to do a PhD.

The conditions for eligibility of home fees status are complex and you will need to seek advice if you have moved to or from the UK (or Republic of Ireland) within the past 3 years or have applied for settled status under the EU Settlement Scheme.  

Entry requirements

Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate quantitative subject such as mathematics, statistics, machine learning, computer science, bioinformatics, physics or econometrics.

If English is not your first language you will need to meet the English language requirements and provide proof of proficiency. Click here for more information.

How to apply

Apply now

To apply, please click the ‘Apply Now’ button above. In the application process you will be asked to upload several documents

•            CV

•            Letter of application (outlining your academic interests, prior research experience and reasons for wishing to undertake the project).

•            Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)

•            Names of two referees familiar with your academic work. You are not required to obtain references yourself. We will request references directly from your referees if you are shortlisted.

•            If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English.

The closing date for applications is midnight on 16/03/2025. 

All application documents must be submitted in English. Certified translated copies of academic qualifications must also be provided.

Please quote reference 5486 on your application and in any correspondence about this studentship.

Summary