The HDR UK-Wellcome Biomedical Vacation Scholarship (BVS) programme is designed to support undergraduates, in the middle year(s) of quantitative degrees, to undertake health data research projects for the very first time.

Our programme offers a selection of exciting and highly challenging research projects hosted by organisations from across the UK which are to be carried out during the 2023 summer vacation. These will give insights into scientific research and the opportunity to work under leading UK academics and clinicians.

As the national institute for health data research we promote diversity among health data researchers. The HDR UK-Wellcome scheme prioritises those from socio-economic groups that are under-represented in health data science, including those attending non-Russell Group universities.

The projects will be for 6-8 weeks during summer 2023.

Find out more about the application process

Throughout the internship, Scholars will receive:

  • the Real Living Wage, plus holiday pay and NI contributions
  • up to £1,500 (or £2,000 if living in London) towards travel and accommodation if required
  • up to £500 towards materials and equipment costs
  • A mentor
  • Training on health data skills from HDR UK academics

Meet our 2022 scholars

Our 2023 projects:

  • Project supervisor: Dr Jinming Duan

    Internship mode: Flexible, to be agreed with scholar

    Cardiovascular MR (CMR) imaging enables quantification of the heart, which are crucial for diagnosing, assessing and monitoring cardiovascular diseases (CVDs). A limitation of CMR is the slow acquisition time, which makes the tool costly and less accessible to worldwide population. Accelerating the CMR acquisition is therefore essential. However, reconstructing high-quality images from accelerated CMR acquisition is a non-trivial problem. Another limitation is quantitative analysis of reconstructed CMR images requires the development of separate post-processing methods. The resulting quantification can be inaccurate if the
    reconstruction contains errors.

    As such, we aim to develop end-to-end, optimal AI and machine learning approaches that bypass the usual image reconstruction stage, therefore improving both the CMR acquisition time and quantification accuracy. The key insight here is that in many cases the images are not an end in themselves, but rather the means of accessing clinically relevant parameters. Therefore, it is more effective to instead combine reconstruction and post-processing steps and learn an end-to-end, optimal model that directly calculates final results as accurately and efficiently as possible. Consequently, patients with CVDs is poised to benefit from fast yet accurate diagnosis as well as better prognosis of outcome and recovery, leading to improved healthcare wellbeing.

  • Project supervisor: Dr Anthony Wilson

    Internship mode: Hybrid

    There are a growing number of ‘wearable’ vital signs monitors that have the potential to revolutionise the care of patients in hospital or at home. These monitors include patch-based sensors, wristwatch and ring-type devices. They typically measure heart rate, respiratory rate, oxygen levels, temperature and blood pressure. Some monitors employ traditional measurement methods whereas others use novel techniques to measure the vital signs.

    We have created a ‘wearables lab’ at our hospital with the aim of assessing the validity of wearable vital signs monitors in comparison to standard hospital monitoring. In this project the student would compare the measurements from different wearable devices in healthy volunteers who will act as simulated patients. Depending on the progress made, there may be an option for the student to then analyse real world data from EMBRaCE-GM ( NCT05099237) an ongoing clinical study of wearable monitors in cancer patients at our institution.

  • Project supervisor: Dr Jamal Nasir

    Internship mode: Hybrid

    The availability of the genetic code for tens of thousands of patients with rare diseases and cancers through the UK 100,000 Genomes Project, delivered by Genomics England, provides unprecedented access to genetic information, which will help resolve the diagnostic bottleneck for thousands of patients with rare diseases. We have projects registered with Genomics England in the areas of cardiovascular and neurological conditions.

    We are currently investigating a family with a complex neurodevelopmental conditional and additional families with intellectual disability and cardiovascular disease. We have already identified relevant candidate genes, but require additional bioinformatics analysis using Genomics England data, to confirm the disease-causing genes for further investigations. We have a well-established record over the last 10 years in using Next Generation Sequencing (NGS) data to identify and characterise disease related genes. We are aiming to develop our disease gene discovery pipeline and better evaluate genotype-phenotype correlations using recently available prediction software. A dedicated person assisting with this project will make a big difference in moving this work forward and will be given help to perform these analyses alongside use of machine learning and promoter/enhancer predictor tools, BPNet and ABC model, respectively.