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

We are looking for applicants with an enthusiasm to learn how their mathematical and/or computational skills can be applied to biomedical and health care research problems.

Our programme offers a selection of exciting and highly challenging research projects hosted by organisations from across the UK, carried out during the summer break and in-person. They give insights into scientific research and the opportunity to work under leading UK academics and clinicians.


“It was interesting working with such large amounts of data as I had never done this before, and working with big data sets is definitely a good skill to have in data science,” Emma Hesketh, BVS scholarship at Manchester Royal Infirmary.

 



It’s certainly very challenging and very interesting. And because it’s research, no one knows the answers, so we have to figure it out as we go. It has given me really, really good experience and will be very helpful in helping me to decide whether or not I want to progress into research.” Zhe Ren Ooi, UCL intern


Throughout the internship, you receive:

  • The Real Living Wage (£12/hour), plus holiday pay and NI contributions
  • Up to £1,500 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

The projects will be in-person for 6-8 weeks during the summer, with the starting date to be mutually agreed upon between the supervisor and the intern.

2024 projects

  • Host: Northumbria University – Bing Zhai (Computer and Information Sciences)

    This research proposal focuses on an innovative summer internship project designed to develop a data processing pipeline for transforming 3D optical motion capture data into wearable sensor data, including accelerometer and gyroscope outputs, for individuals with Parkinson’s disease (PwP). The project’s first objective is to construct a pipeline that converts 3D optical positional data into SMPL-based motion data utilising existing tools such as Soma and Mosh++. The second objective involves using the PIP to derive wearable sensor data from the SMPL models, enabling human motion reconstruction in a format accessible for real-world application. The revised final goal is to assess the existing motion synthesis algorithms’ performance in reconstructing human motion for the PwP population by identifying and understanding the error margins when these algorithms are applied to patient data. This adjustment shifts the focus from refining algorithms to a comprehensive evaluation of their current capabilities and limitations, offering valuable insights for future enhancements. By analysing clinical gait assessment data from individuals with Parkinson’s, this project seeks to bridge the gap in research that often overlooks the specific needs of this population, thereby enhancing the understanding of disease progression and fall risk assessment through improved data analysis techniques.

  • Host: University of Newcastle – Chris Marshall (NIHR Innovation Observatory)

    Robust clinical trials are crucial for rigorously evaluating the efficacy and safety of novel health interventions., However, the premature termination of these trials can result in significant research waste, depriving health systems of crucial resource that could be better used elsewhere to enhance patient care and outcomes. Within the NIHR Innovation Observatory, we are developing an advanced machine learning approach to predict the likelihood of a trial’s success, trained using our internal OpenScan clinical trial dataset. We are excited to offer undergraduate students the opportunity to engage with this innovative work and contribute to the following objectives:

    Objectives:

    • To explore and apply feature engineering techniques to distil pertinent features from the OpenScan dataset to drive accurate predictions.
    • To support building and refinement of state-of-the-art machine learning algorithms, including regression models, tree models, deep learning models to develop predictive models.
    • To assess the interpretability of these models to identify the critical factors that drive the success rates of clinical trials and provide insights that could support future trial design.

    Supervisors: Dr Hongbo Bo, Dr Saleh Mohammed, Ross Fairbairn and Dr Christopher Marshall

  • Host: University of Newcastle – Carlos Gonzalez (School of Computing)

    Algorithmic fairness is a key piece of Responsible Artificial Intelligence (RAI). This proposal aims to investigate the fairness of resource allocation among multiple hospitals within the UK National Health Service (NHS), to improve the overall quality of healthcare services. By leveraging NHS data sets, the aim is to identify disparities, assess the effectiveness of current allocation strategies, and to propose alternative, data-driven allocations that improve the common good. This project consists of four objectives:

    1. Evaluate the distribution of resources (staff, funding, equipment) across hospitals in the UK.
    2. Identify socio-demographic factors influencing resource allocation patterns.
    3. Assess the impact of resource allocation on healthcare outcomes and patient satisfaction.
    4. Propose data-driven recommendations to enhance the fairness and efficiency of the resource allocation processes.

    Methodology:

    Utilising anonymised NHS data sets , we will perform statistical analyses to analyse resource allocation trends. Machine learning models will be used to assess the association between demographics, resource allocation, hospital characteristics, and patient outcomes. Additionally, geographical mapping will visualise regional disparities in resource distribution. Finally, algorithmically-generated resource distributions satisfying standard fairness definitions, e.g., equality of opportunity and equality of outcomes, will be proposed.

    Significance:

    Findings will inform policymakers, healthcare administrators, and stakeholders on strategies to optimise resource allocation processes, ultimately enhancing healthcare accessibility and quality across the UK.

    Conclusion:

    This project seeks to uncover insights that result in a fairer and more effective allocation of resources among UK hospitals, thus advancing the overarching goal of equitable healthcare provision.

Eligibility

  • Undergraduate degree in a quantitative subject
  • At a UK or Irish university
  • In the middle year(s) of your degree
  • Interested in health data research
  • Full-time internship only (~37.5 hours / week)
  • International students that meet above criteria are welcome to apply

Examples of relevant subjects

  • Computer / data science
  • Engineering (all types)
  • Mathematics / Statistics
  • Physics, Chemistry or other quantitative sciences

Your undergraduate subject must be quantitative – that is, it must contain a substantial element of statistics and / or computing.

Candidates can apply for other Wellcome-funded internship programmes

Unfortunately we cannot accept your application if you:

  • have previously undertaken a vacation scholarship from Wellcome or another funding body, or have had significant research experience
  • have completed or are currently undertaking an intercalated year
  • have completed or are currently undertaking a one-year placement in research as part of your degree (e.g. a sandwich year)
  • are a graduate-entry medical student who has completed a previous undergraduate degree in a science-related subject
  • are a first year or final year student
  • are looking for funding for external health data research projects
  • are looking for a remote-based internship

The application process

Applications are currently: Closed for 2024

  • Whilst the internship is open to eligible candidates, we are prioritising applications from individuals who are underrepresented within health data research. This includes:

    • If you are in the first generation of your family to attend university
    • If you are or have been a looked after person (Care-experienced)
    • If you are a carer (i.e. someone who cares, unpaid, for a friend or family member who due to illness, disability or for another reason cannot cope without your support)
    • If you are a government refugee or asylum seeker
    • If you have a clinically diagnosed disability
    • If you were eligible for Free School Meals

    We will also encourage applications from those who are studying at a non-Russell Group university. Fulfilling one or more of the criteria above will mean that your application will be prioritised for review.

    If you are awarded a scholarship and have declared that you meet one or more of the criteria listed above you will be expected to evidence this either via a supporting statement from a referee, formal documentation or otherwise.

    Please note: This information will not be made available to anyone in the shortlisting and interview process. It will only be viewed (and noted) by restricted administrative staff to ensure we can support you through the application process.

  • Applications open: Monday 22 April 2024

    Deadline: Sunday 12 May 2024 23:59

    Interviews: Late May – June 2024

  • We accept applications on our application portal. You will be asked about the following in your application form to us:

    • Your contact details (name, email address, phone number)
    • An up-to-date CV
    • Your current degree subject
    • Eligibility questions (as set by Wellcome)
    • Your research project choice
    • Criteria for widening access
    • 4 personal statement-style questions on your experiences, skills and motivation

    Please note that there is a timed assessment within this application. We will be asking 3 Maths-based questions with 15 minutes. If you have any reasonable adjustments that require extra time, please email us at phd@hdruk.ac.uk before opening this assessment.

    Please ensure that your university details (course, dates and university) are up-to-date to ensure proof of your eligibility for our programme. We would also recommend checking the contact details (phone and email) on your CV are current and match those in your application.

    There is no need to contact a supervisor prior to submitting your application.

  • After your application has been submitted, we will be screening applications for eligibility before reviewing personal statement-style answers and timed assessment answers. This will be done in a blind and randomised manner to review applications in a fair manner.

    Shortlisted applicants will be contacted for an interview with host organisation academics, PhD students and HDR UK Training staff. Those who pass the sifting process and do not reach interview will receive feedback on their personal statement-style answers. All candidates who complete an interview within our scheme can receive feedback, regardless of outcome.

    We are looking for applicants with an enthusiasm to learn how their mathematical and/or computational skills can be applied to biomedical and health care research problems.

    We will look for these:

    • In your Maths quiz scoring (at least 2/3)
    • In the answers you give to the personal statement-style questions in your application to us, which are then reviewed anonymously and given a score

    The start date of the internship will be mutually agreed between the supervisor and the incoming intern. It must take place during summer 2024 for 6-8 weeks only.

Thinking about our 2025 programme? Register your interest below:

Got any questions?

If you have any questions about the recruitment process, or about the programme, please feel welcome to reach out to us via email below or phone (0770 847 8846).

Email us

 

Meet our previous scholars

A note on positive action

This scholarship programme is a form of positive action. Positive action is a range of measures permitted under the Equality Act 2010 which can be lawfully taken to encourage and train people from under-represented groups to help them overcome disadvantages in competing with other applicants. Applicants eligible for the Biomedical Vacation Scholarship are from groups which are under-represented in postgraduate research, with the aim of increasing participation in postgraduate research from these groups.