Data-driven solutions to improving cancer diagnosis

Implementation of cancer risk prediction models in practice, to identify and test people at increased risk of undetected cancer. Data analysis to develop models for healthcare data for cancer early diagnosis.

Start date

1 October 2022

Duration

4 years

Application deadline

Funding source

University of Surrey, Doctoral College

Funding information

  • Home fees are covered by the studentship
  • You will be paid an enhanced EPSRC stipend of £19,023 per year
  • £3,000 allowance for studentship-related expenses is also available
  • The duration of the PhD and funding is for 4 years 
  • The studentship will be based at the University of Surrey.

About

This is a two-pronged project. The student will develop skills running small clinical studies that implement data-driven prediction algorithms to identify and test for cancer people who are at an increased risk. The focus will be on pancreatic cancer. The prediction algorithms already exist, they were developed for routine data, and they could be used to identify people at risk and test them for cancer (real-life). Currently many people with pancreatic cancer are diagnosed too late for curative treatment and therefore we need data-driven approaches to support early diagnosis. The student will develop skills in setting up clinical studies. They will write a protocol and design all the study materials (including patient information leaflet), they will obtain ethics approval and conduct the study in clinical practice. We therefore request that the student has excellent communication skills and is willing to engage with clinicians and patients.

The second part of this project will involve data analysis. Routinely collected healthcare data and national registers will be analysed to model trends in data, for example to identify cancer symptoms that could help in early diagnosis or produce cancer risk prediction models. The student will be encouraged to publish papers based on their data work, to produce software open access and to gain skills in working with healthcare data. We therefore request that the prospective students that apply for this studentship have some expertise in data analysis, skills and keen interest to work with large healthcare data and skills in computer programming.

Skills and abilities desired

  • Science, maths, computer sciences or healthcare degree
  • Communication skills, ability to engage with clinicians and patients
  • Data analysis and programming skills
  • Interest in healthcare data and analysis of large datasets.

Open to UK students starting in October 2022. Later start dates are possible.

Additional notes

This project will be jointly based at the University of Surrey and at the National Physical Laboratory (NPL). NPL is the UK's national measurement institute based in Teddington and a strategic partner of the University of Surrey.

Eligibility criteria

Open to UK students starting in October 2022. Later start dates are possible.

Applicants are expected to hold a good honours degree (upper second) in an appropriate discipline, but prior experience in statistics, data science or healthcare-related research may be acceptable.

English language requirements

IELTS Academic 6.5 or above (or equivalent) with at least 6.0 in each individual category, or equivalent.

How to apply

Applications should be submitted via the Health Sciences PhD programme, please select October 2022 start date. Please state the studentship title and the name of the first supervisor: Dr Agz Lemanska. Please email a.lemanska@surrey.ac.uk to advise of your application.


Application deadline

Contact details

Agnieszka Lemanska
DK 04
Telephone: +44 (0)1483 689384
E-mail: a.lemanska@surrey.ac.uk
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