Undergraduate Summer Internship in Health Data Research

• NIHR Exeter Biomedical Research Centre - Finding the “why?” in medical data to improve trust in stroke pathway simulations

  Primary supervisor: Dr Anna Laws 

  Secondary supervisor: Dr Mike Allen 

  Location: University of Exeter Medical School, Exeter 

  Internship mode: Hybrid 

  Duration: 6 weeks, 8 June – 17 July 2026 

  Project descriptor: Looking at the big picture across emergency stroke patients in England and Wales, the outcomes after stroke depend on the patient details, the stroke pathway and timings, and treatment decisions.  

  However, the data only reveals trends rather than the underlying causes of good outcomes. For example, did a patient have a good outcome because of treatment, or would they also have done so without treatment? Being able to fully answer why certain results happen will improve the trust of patients and clinicians in the conclusions drawn from big data and applied to simulations of the stroke pathway. 

  In response, we are trialling an approach that emphasises the “why” from the start. We start with no assumptions about links in the stroke data. Then causal discovery methods can uncover the links and so find which factors drive good outcomes. 

  This project is designed to test whether causal discovery methods can give trustworthy results for tabular medical data such as the stroke data. The intern will generate new data with known links between attributes, apply existing causal discovery methods, and check the accuracy of the inferred structures. We will visualise the results for non- technical audiences using network graphs. 

  Learning outcomes and training: Through this project, the student will learn how to work methodically by testing different scenarios in a structured way and keeping clear records of their results. They’ll also develop the ability to create clear, accessible graphs that communicate findings effectively to both clinicians and the general public. 

  Desirable background knowledge/skillsets: All applicants are encouraged to apply, regardless of prior experience, and there are no essential technical requirements or background knowledge required for this opportunity. However, having an academic background with some quantitative elements can be helpful. Any coding experience, such as using R or Python, may also be useful, but it is not expected that applicants will have experience in causal discovery methods or network graphs. 

   

• NIHR Exeter Biomedical Research Centre - Visualising digital technology-derived data for understanding neurodegenerative conditions

  Primary supervisor: Professor Yinghui Wei 

  Secondary supervisor: Dr Mike Allen 

  Location: University of Exeter Medical School, Exeter 

  Internship mode: Hybrid 

  Duration: 6 weeks, 8 June – 17 July 2026 

  Project descriptor: Neurodegenerative conditions such as Parkinson’s disease and Alzheimer’s disease are progressive disorders that involve a decline in cognitive, motor, and functional abilities. As symptoms evolve over time, effective monitoring is crucial for understanding disease progression, and improving quality of life for affected individuals. 

  Traditional clinical assessments rely largely on in-person evaluations, which can be subjective and limited by time and resource constraints. Advances in digital technologies enable the remote collection of patient data from sources such as speech recordings, handwriting tasks, and cognitive tests. These data have the potential to reveal signs of changes that may not be captured in traditional assessments. Data visualisation can help understand complex, multidimensional datasets. Well-designed visualisations can highlight patterns, differences between individuals with and without a condition, and potential trajectories of change.  

  This project aims to develop data visualisation approaches to support the understanding of neurodegenerative conditions using individual-level data collected through digital technologies. The intern will explore a digital technology-derived dataset, examine its structure, and apply visualisation techniques to identify patterns, differentiate patient groups and illustrate indicators of potential disease progression.  

  Learning outcomes and training: Through this project, the student will learn how to use visualisation techniques to make sense of complex data and identify meaningful patterns. They’ll develop the ability to interpret results within the appropriate scientific and clinical context, strengthening their analytical judgement. Additionally, gain experience in conducting reproducible research, working in a structured, transparent way and communicating their methods and findings clearly. 

  Desirable background knowledge/skillsets: All applicants are encouraged to apply, regardless of prior experience, and there are no essential technical requirements or background knowledge required for this opportunity. However, an academic background that includes some quantitative elements can be helpful. Similarly, any experience with coding, whether in R, Python, or another programming language, would be a useful addition. 

   

• NIHR Exeter Biomedical Research Centre - Classification of Diabetic Foot Ulcers (DFU) from people with diabetes using data from Plantar Pressure measured using insoles

  Primary supervisor: Associate Professor Neil Vaughan 

  Secondary supervisor: Professor Yinghui Wei 

  Location: Research Innovation Learning & Development Building, Exeter 

  Internship mode: In-person or hybrid options available 

  Duration: 12 weeks, start date to be mutually agreed upon acceptance 

  Project descriptor: The aim of this project is to analyse existing plantar pressure data. The data includes people with and without diabetes, neuropathy and Diabetic Foot Ulcers (DFU). We will apply machine learning to classify the individuals as DFU or Not-DFU or as a DFU severity scale. As well as classification we will use ML methods to identify other patterns in the pressure between individuals, between left and right foot and between the groups of people with and without diabetes, neuropathy and DFU. We will also investigate other signs of DFU, including uneven pressure between left and right feet. We have obtained various types of datasets including shear pressure (Haron et al., 2024) and normal plantar pressure that can be analysed. There is also the option to obtain new plantar pressure data, using the digital plantar pressure measuring insoles we have available. 

  Learning outcomes and training: The student will have the opportunity to work closely with our data analysis team on the interpretation of plantar pressure data, using several rich existing datasets. They will be supported by example Python and MATLAB scripts already developed within the group, providing clear starting points for exploration and analysis. Collaboration with PhD students experienced in this area offers additional insight into generating meaningful visualisations and understanding the clinical relevance of the data. The analytical and visualisation skills gained through this work are highly transferable, equipping the individual with capabilities they can apply to a wide range of future health data projects. 

  Desirable background knowledge/skillsets: All applicants are encouraged to apply, regardless of prior experience, and there are no essential technical requirements or background knowledge required for this opportunity. However, basic data skills such as working with common file types or using simple scripts in any programming language can be helpful. Likewise, having some general awareness of health topics can be beneficial, but no detailed knowledge of particular conditions is needed. 

   

• NIHR Exeter Biomedical Research Centre - Using AI prioritisation to discover novel genetic causes of neonatal diabetes

  Primary supervisor: Associate Professor Elisa De Franco 

  Secondary supervisor: Dr Thomas Laver 

  Location: Research Innovation Learning & Development Building, Exeter 

  Internship mode: In-person or hybrid options available 

  Duration: 12 weeks, start date to be mutually agreed upon acceptance 

  Project descriptor: Neonatal diabetes (NDM) is a rare form of diabetes diagnosed in the first 6 months of life. NDM differs from type 1 and type 2 diabetes as it is caused by single, rare variants in specific genes. Identifying these genes is important to manage the patients’ disease and it can give new biological insights into type 1 and type 2 diabetes.  

  In this project, the student will analyse exome/genome sequencing data from our unique cohort of 316 individuals with NDM without a known genetic cause. Specifically, the student will  

  1. Trial the use of state-of-the-art AI prioritisation software such as AIMARRVEL to highlight candidate variants to be followed up  

  1. Assess the prioritised variants using a variety of established approaches 

  1. Interpret the variants using clinical phenotyping to decide if they are indeed the cause of the patient’s NDM.  

  The student will receive training in handling genomic data, using the different tools involved in data analysis, and in performing variant assessment and interpretation. 

  This project is a great opportunity for a student interested in human genomics and bioinformatics to gain hands-on experience with genomic data analysis, contributing to the discovery of novel genetic causes of NDM. 

  Learning outcomes and training: The student will be fully embedded within our multidisciplinary research team, which brings together bioinformaticians, molecular geneticists and clinicians. They will participate in weekly research group meetings and have the opportunity to attend wider departmental seminars, gaining exposure to a vibrant academic community. Dedicated training will be provided by both supervisors, supporting the development of bioinformatics skills for DNA sequencing analysis, alongside working with patient clinical data. Together, they will teach the use of internationally recognised guidelines for interpreting whether genetic variants are disease‑causing. This project includes the exciting opportunity for the student to be included as an author on scientific publications. 

  Desirable background knowledge/skillsets: All applicants are encouraged to apply, regardless of prior experience, and there are no essential technical requirements or background knowledge required for this opportunity. However, a general understanding of human genetics and particularly how genetic variants can contribute to rare diseases, would be a beneficial addition. Some familiarity with using terminal or command‑line tools may also be helpful for aspects of the project. 

   

• NIHR Exeter Biomedical Research Centre - Using copy-number variant analysis to reveal novel genetic causes of insulin secretion disorders

  Primary supervisor: Dr Oguzhan Kalyon 

  Secondary supervisor: Dr Thomas Laver 

  Location: Research Innovation Learning & Development Building, Exeter 

  Internship mode: In-person or hybrid options available 

  Duration: 11 weeks, 1 June – 14 August 2026 

  Project descriptor: This project will suit a student interested in applying data science to real-world genomic and clinical datasets. The intern will investigate copy-number variants (CNVs) in patients with insulin secretion disorders (including diabetes and hyperinsulinism) to identify variants that may explain their disease. 

  The student will: (1) familiarise themselves with CNV biology and existing literature on CNVs in insulin secretion disorders; (2) work with pre-computed CNV callsets and, where appropriate, run or refine CNV detection tools on sequencing data; (3) annotate CNVs using databases such as gnomAD, DECIPHER, ClinVar, ClinGen dosage sensitivity data, DGV and genome browsers (UCSC/Ensembl); (4) use R or Python to summarise and visualise CNV results across the cohort; and (5) integrate phenotype and gene-level information to prioritise candidates for clinical follow-up. 

  The student will gain experience of health data science workflows, variant interpretation frameworks, and communicating findings to a multi-disciplinary team of clinical scientists, bioinformaticians and clinicians. There is potential for the student to contribute to a manuscript or conference abstract if results are suitable. 

  Learning outcomes and training: Through this project, the student will gain hands‑on experience working with large genomic and clinical datasets in a secure environment. They receive practical training in Copy Number Variation (CNV), CNV calling, and variant interpretation related to insulin‑secretion disorders. They’ll learn to use key genomics tools such as IGV, basic command‑line utilities, and major genomic databases. They also develop skills in R or Python for exploring, checking, and visualising CNV data. Alongside this technical training, the student will build an understanding of how gene–disease links are identified and how genomic results contribute to diagnosis. Working within a multidisciplinary team, they share their findings in lab meetings and receive supportive feedback to strengthen their communication skills. 

  Desirable background knowledge/skillsets: All applicants are encouraged to apply, regardless of prior experience, and there are no essential technical requirements or background knowledge required for this opportunity. However, a general understanding of human genetics and particularly how genetic variants can contribute to rare diseases, would be a beneficial addition. Some familiarity with using terminal or command‑line tools may also be helpful for aspects of the project. 

   

• University College London - High-fidelity 3D reconstructions of paediatric hearts and associated vessels using magnetic resonance (MR) and computed tomography (CT)

  Primary supervisor: Professor Silvia Schievano 

  Secondary supervisor: Dr Claudio Capelli 

  Location: The Zayed Centre for Research into Rare Disease in Children, London 

  Internship mode: In-person or hybrid options available 

  Duration: 10 weeks, start date to be mutually agreed upon acceptance 

  Project descriptor: This internship focuses on creating 3D patient-specific models of paediatric hearts and vessels using MR and CT scans from children with congenital heart disease. Working alongside clinicians and researchers, the intern will learn to process medical imaging datasets, perform anatomical segmentation, and reconstruct detailed 3D geometries of cardiac structures. The resulting models will be used to support teaching and training, including fabrication of realistic 3D-printed heart models and integration into virtual-reality platforms for advanced anatomical exploration. The project offers exposure to medical imaging, computational modelling, and digital fabrication workflows, providing hands-on experience across engineering, biomedical science, and clinical translation. This is an excellent opportunity for students interested in cardiovascular disease, healthcare technologies, image-based modelling, or visualisation methods. The intern will contribute meaningfully to a growing programme focused on improving understanding of complex congenital heart disease through interactive and accessible 3D representations. 

  Learning outcomes and training: The internship will provide training in medical image processing and 3D modelling. The student will have the opportunity to learn about cardiovascular anatomy, especially in congenital heart disease, and experience collaborative research in healthcare, including interdisciplinary meetings and patient/ public communication engagement activities. 

  Desirable background knowledge/skillsets: All applicants are encouraged to apply, regardless of prior experience, and there are no essential technical requirements or background knowledge required for this opportunity. However, if you have an interest in medical engineering, biomedical imaging, or related areas, this would be a helpful advantage. A basic understanding of anatomy, particularly cardiovascular anatomy, can also be beneficial but is not essential. Motivation to learn new digital tools and engage collaboratively with clinical partners would be a great bonus. 

   

• University College London - Longitudinal studies of heart and brain – how to identify and address potential sources of bias

  Primary supervisor: Dr Scott Chiesa

  Secondary supervisor: Associate Professor Carole Sudre

  Location: Department of Population Science and Experimental Medicine, London 

  Internship mode: In-person 

  Duration: 7 Weeks, start date to be mutually agreed upon acceptance

  Project descriptor: Heart and brain health go hand in hand throughout life. To better understand the relationship between characteristics of those organs and bad outcomes such as decline in brain function, the start of dementia and even death, one must be able to accurately assess changes over time. There are however many hurdles to doing this properly that can, if not well taken care of, lead to wrong interpretation of the data. In this project, we will explore different sources of bias in multiple longitudinal cohorts when considering our evaluation of the heart-brain relationship over time. We will notably explore how existing factors (ill-health, ethnic and socio-economic background) affect continued participation in a longitudinal study and completion of clinic visits. If time allows we will also explore how the processing of the acquired data may impact our findings.

  Learning outcomes and training: You will gain a clear understanding of the data lifecourse, from how information is collected from individuals to how it is processed and interpreted at scale. The student will have the opportunity to observe data collection in practice and learn how raw measurements evolve into research‑ready datasets. Conversations with experts in epidemiology and medical imaging providing valuable real‑world context, alongside developing awareness of key sources of bias, including survival and attrition bias, processing bias, and recruitment bias, and how these can influence research outcomes.

  Desirable background knowledge/skillsets: All applicants are encouraged to apply, regardless of prior experience, and there are no essential technical requirements or background knowledge required for this opportunity. However, if you have an interest in coding, knowledge or curiosity about statistics, or an interest in neuroimaging and imaging analysis, these skills would be a great bonus. 

• University College London - IM2SIM – A deep learning framework for rapid cardiac digital twin development directly from imaging data

  Primary supervisor: Professor Vivek Muthurangu 

  Secondary supervisor: Dr Tina Yao 

  Location: The Zayed Centre for Research into Rare Disease in Children, London 

  Internship mode: In-person or hybrid options available 

  Duration: 12 weeks, start date to be mutually agreed upon acceptance 

  Project descriptor: This internship will introduce the student to image-to-simulation (IM2SIM) approaches for personalised cardiovascular modelling. The project will focus on developing a proof-of-concept automated pipeline that converts cardiac MRI data into simplified, simulation-ready heart models capable of estimating key physiological biomarkers (most of the key framework is already in place). 

  Working with a UCL supervisor at the forefront of deep learning and digital twin research, the student will contribute to a clearly defined subset of the IM2SIM workflow. The core objective will be adapting existing deep learning methods for ventricular segmentation and basic motion estimation from cardiac MRI, which can then be used by the larger team to (2) generate reduced-order cardiac geometries suitable for rapid simulation. 

  The internship will provide hands-on experience in medical image analysis, machine learning, and scientific programming (primarily Python and TensorFlow), alongside exposure to key concepts in computational physiology. The student will also be trained in reproducible research practices, version control, and the responsible use of health data within HDR UK Trusted Research Environments. 

  Depending on progress and interests, there may be opportunities to explore validation using existing clinical datasets or assess scalability with population-level imaging resources. This internship is well suited to students interested in biomedical engineering, computer science, or AI in healthcare. 

  Learning outcomes and training: Through this project, the student will gain a focused introduction to deep learning as it applies to medical imaging and patient impact, exploring how these methods support real‑world clinical applications. They’ll receive specific training in deep learning architectures used for simulation, programming, and model deployment, developing practical insight into how advanced computational tools are built and applied within healthcare. 

  Desirable background knowledge/skillsets: All applicants are encouraged to apply, regardless of prior experience, and there are no essential technical requirements or background knowledge required for this opportunity. However, if you have experience with Python, a basic understanding of deep learning concepts, or familiarity with agile ways of working, these skills would be a great bonus. 

   

• University of Nottingham - Communicating data science research for wider audiences

  Primary supervisor: Associate Professor Grazziela Figuerdo 

  Secondary supervisor: Associate Professor Tim Beck 

  Location: Biodiscovery Institute, Nottingham 

  Internship mode: In-person or hybrid options available 

  Duration: 12 weeks, start date to be mutually agreed upon acceptance 

  Project descriptor: Data science research has the greatest impact when it is clearly communicated to a wide range of audiences, including students, researchers from other disciplines, funders, and the general public. This project focuses on helping to communicate the data science activities, tools, and research carried out within our Centre For Health Informatics in an engaging, accurate, and accessible way. 

  The intern will work closely with researchers to understand ongoing projects and translate technical ideas into clear explanations using written content, visual materials, and digital media. The emphasis will be on storytelling, clarity, and responsible communication of data-driven research. 

  Learning outcomes and training: You will gain experience in science communication, learning how to explain data science concepts without requiring advanced technical knowledge. You will develop skills in summarising research, creating accessible written and visual content, and tailoring messages for different audiences. The project also offers insight into how interdisciplinary data science research is organised and delivered in an academic research centre. 

  Desirable background knowledge/skillsets: All applicants are encouraged to apply, regardless of prior experience, and there are no essential technical requirements or background knowledge required for this opportunity.  However, if you have experience in areas such as systematic literature review, scientific communication and writing, data analysis, data transformation or standardisation, software development, programming (e.g., Python), working with JSON, Artificial Intelligence or Machine Learning, or qualitative and quantitative research methods, these skills would be a great bonus. 

   

• University of Nottingham - Improving an AI tool that understands medical language (federated learning)

  Primary supervisor: Associate Professor Grazziela Figuerdo 

  Secondary supervisor: Associate Professor Tim Beck 

  Location: Biodiscovery Institute, Nottingham 

  Internship mode: In-person or hybrid options available 

  Duration: 12 weeks, start date to be mutually agreed upon acceptance 

  Project descriptor: Healthcare data often use free-text descriptions that need to be translated into standard formats before analysis. This project works with Lettuce, an open-source AI tool that helps convert medical terms into a common standard used in health research. 

  The intern will help test and improve this tool in a federated setting, where data remain at their original sites rather than being pooled centrally. This is important for privacy-preserving health research. 

  Learning outcomes and training: You will gain an introduction to large language models, natural language processing, and federated learning concepts. The project also provides insight into how AI can be applied safely and responsibly in healthcare research. 

  Desirable background knowledge/skillsets: All applicants are encouraged to apply, regardless of prior experience, and there are no essential technical requirements or background knowledge required for this opportunity.  However, if you have experience in areas such as systematic literature review, scientific communication and writing, data analysis, data transformation or standardisation, software development, programming (e.g., Python), working with JSON, Artificial Intelligence or Machine Learning, or qualitative and quantitative research methods, these skills would be a great bonus. 

• University of Nottingham - Discovering patterns in data using clustering (Helix)

  Primary supervisor: Associate Professor Grazziela Figuerdo 

  Secondary supervisor: Associate Professor Tim Beck 

  Location: Biodiscovery Institute, Nottingham 

  Internship mode: In-person or hybrid options available 

  Duration: 12 weeks, start date to be mutually agreed upon acceptance 

  Project descriptor: Many datasets contain hidden patterns that are not obvious at first glance. This project focuses on exploratory data analysis, where the aim is to group similar data points together and uncover structure without knowing the outcome in advance. 

  Using Helix, the intern will help add new features that allow users to explore and visualise patterns in data. This will support researchers in generating new questions and insights before building predictive models. 

  Learning outcomes and training: You will be introduced to unsupervised learning concepts, such as clustering, and gain experience in visualising and interpreting data. The project also provides exposure to developing interactive research software for non-expert users. 

  Desirable background knowledge/skillsets: All applicants are encouraged to apply, regardless of prior experience, and there are no essential technical requirements or background knowledge required for this opportunity.  However, if you have experience in areas such as systematic literature review, scientific communication and writing, data analysis, data transformation or standardisation, software development, programming (e.g., Python), working with JSON, Artificial Intelligence or Machine Learning, or qualitative and quantitative research methods, these skills would be a great bonus. 

   

• University of Nottingham - Learning data science by improving a research analysis tool (Helix)

  Primary supervisor: Associate Professor Grazziela Figuerdo 

  Secondary supervisor: Associate Professor Tim Beck 

  Location: Biodiscovery Institute, Nottingham 

  Internship mode: In-person or hybrid options available 

  Duration: 12 weeks, start date to be mutually agreed upon acceptance 

  Project descriptor: This project introduces students to the fundamentals of data science by working with Helix, a user-friendly research software tool used to analyse scientific datasets. The intern will learn how raw data are prepared before analysis and will help add new ways of cleaning and transforming data so that it can be analysed more effectively. 

  The focus is on gaining hands-on experience with real research software, understanding why different data preparation choices matter, and contributing practical improvements to an existing open-source tool used by researchers. The intern will work on features that allow entire analysis workflows to be restored from saved files, helping ensure transparency and consistency across projects and users. 

  Learning outcomes and training: You will gain an introduction to data science workflows, basic programming in Python, and research software engineering practices. You will also develop an understanding of how data preparation affects the quality and reliability of analysis results. 

  Desirable background knowledge/skillsets: All applicants are encouraged to apply, regardless of prior experience, and there are no essential technical requirements or background knowledge required for this opportunity.  However, if you have experience in areas such as systematic literature review, scientific communication and writing, data analysis, data transformation or standardisation, software development, programming (e.g., Python), working with JSON, Artificial Intelligence or Machine Learning, or qualitative and quantitative research methods, these skills would be a great bonus. 

   

• University of Nottingham - Understanding security and trust in sensitive research data platforms (TREs)

  Primary supervisor: Associate Professor Grazziela Figuerdo 

  Secondary supervisor: Associate Professor Tim Beck 

  Location: Biodiscovery Institute, Nottingham 

  Internship mode: In-person or hybrid options available 

  Duration: 12 weeks, start date to be mutually agreed upon acceptance 

  Project descriptor: Trusted Research Environments (TREs) are secure platforms that allow researchers to work with sensitive data, such as health or social data, while protecting privacy and meeting legal requirements. This project focuses on understanding what makes software secure and trustworthy in these environments. 

  The intern will review existing guidance and best practices and help produce a clear, accessible document describing security requirements for software used in TREs. 

  Learning outcomes and training: You will develop an understanding of data governance, security principles, and ethical considerations in data science. The project also builds skills in critical reading, structured writing, and translating complex technical guidance into clear documentation. 

  Desirable background knowledge/skillsets: All applicants are encouraged to apply, regardless of prior experience, and there are no essential technical requirements or background knowledge required for this opportunity.  However, if you have experience in areas such as systematic literature review, scientific communication and writing, data analysis, data transformation or standardisation, software development, programming (e.g., Python), working with JSON, Artificial Intelligence or Machine Learning, or qualitative and quantitative research methods, these skills would be a great bonus. 

   

• University of Sheffield - Data correlation of endoscopic instrument insertion and interaction forces with the endoscopy simulator

  Primary supervisor: Professor Sanja Dogramadiz 

  Secondary supervisor: Michael Evans 

  Location: Amy Johnson Building, Sheffield 

  Internship mode: Hybrid 

  Duration: 10 weeks, 15 June – 31 August 2026 

  Project descriptor: There is an increasing number of endoscopists performed in hospitals that require prolonged training of junior doctors. Their training is performed on physical phantoms and learning is supported by observations of experienced colleagues. Qualitative studies and evaluations of trainees skills are currently not possible. This project aims to establish correlations between endoscopists’ instrument manipulation and its effect on the instrument progress through the model. 

  Learning outcomes and training: Through this project, the student will have opportunities to understand endoscopic procedures and the demands they place on endoscopists, offering valuable insight into how clinical practice shapes and is shaped by data. They’ll also develop skills in analysing data within this clinical context and learn how to identify relationships between diverse data streams, helping them understand how different types of information can be combined to generate meaningful insights. 

  Desirable background knowledge/skillsets: All applicants are encouraged to apply, regardless of prior experience, and there are no essential technical requirements or background knowledge required for this opportunity. However, an interest in working with different types of data and exploring statistical approaches can be beneficial. Analytic skills and familiarity with statistical software, such as MATLAB or similar tools, may also be helpful. 

   

• University of Sheffield - Comparative ergonomic study of clinical gastroenterologists using standard endoscopic instruments

  Primary supervisor: Professor Sanja Dogramadiz 

  Secondary supervisor: Zeyu Song 

  Location: Amy Johnson Building, Sheffield 

  Internship mode: Hybrid 

  Duration: 10 weeks, 15 June – 31 August 2026 

  Project descriptor: There is a rising prevalence of musculoskeletal pain and injuries (MSPI) in endoscopists, reported in up to 89%of operators. Endoscopy ergonomics has hence emerged as an area of interest to prevent and reduce endoscopy associated MSPI. Endoscopic procedures are associated with repetitive movements, particularly of the fingers, wrists and upper limbs. It is believed that resulting injuries are due to the poor ergonomics of the one-size-fits-all endoscope design. This study will analyze data related to endoscopists’ hand and arm movements during colonoscopy procedures in order to determine the effects on endoscopists with different hand sizes and skill levels. An improved understanding of these measures will enable improved ergonomics during endoscopy and educational interventions such as simulation–based training.  

  Learning outcomes and training: Through this project, the student will have opportunities to understand endoscopic procedures and the demands they place on endoscopists, offering valuable insight into how clinical practice shapes and is shaped by data. They’ll also develop skills in analysing data within this clinical context and learn how to identify relationships between diverse data streams, helping them understand how different types of information can be combined to generate meaningful insights. 

  Desirable background knowledge/skillsets: All applicants are encouraged to apply, regardless of prior experience, and there are no essential technical requirements or background knowledge required for this opportunity. However, an interest in working with different types of data and exploring statistical approaches can be beneficial. Analytic skills and familiarity with statistical software, such as MATLAB or similar tools, may also be helpful. 

   

Application top tips

• Understanding the purpose of the application

  Your application is not a test of how much you already know, it is an opportunity for us to understand your potential, your motivation, and the direction you hope to grow in. HDR UK and your host organisation read your application to learn what excites you, how you think, and what has shaped your academic journey so far.

  We are most interested in your curiosity, your commitment to learning, and the way you reflect on your experiences whether these are academic, personal or extracurricular. You do not need prior data science or research experience to be a strong candidate.

  Your goal is to help us see:

  • why this opportunity appeals to you,
  • what you hope to gain,
  • how you approach challenges and learning, and
  • why this internship feels like the right next step for you.

  We’re not looking for polished professionals. We’re looking for promising students with enthusiasm and drive.

• Personal style statement questions

  The personal style statement questions are one of the most important parts and will be the only element of your application we review in the initial sifting stage. While your CV provides the evidence of what you’ve done, these written responses show us who you are, how you think, what motivates you, and how you approach learning and challenges.

  This is where your motivation, potential and reflective ability shine through.

  Draft your answers

  We strongly recommend copying the three competency style questions into a Word document so that you can:

  • draft and redraft your responses
  • check your word count
  • proofread carefully
  • ensure you’re expressing yourself clearly

  Use the STAR method to structure your answers

  Competency questions ask you to reflect on your experiences where you’ve demonstrated a relevant skill or behaviour. To do this effectively, use the STAR method:

  • S – Situation: Briefly set the scene. What was happening?
  • T – Task: What was the goal, challenge or responsibility?
  • A – Action: What you did. Focus on your decisions, contributions and thought process.
  • R – Result: What happened? What did you learn? What changed as a result?
    Remember, the “Action” section is the most important. Avoid saying “we” unless you explain your specific role within the group.

  Choose strong, relevant examples

  You do not need experience in data science or health research. Draw upon any experiences you think relevant, whether that’s during your academic studies or in your personal life – these are still powerful if you reflect on them meaningfully.

  Pick examples that show skills linked to the internship, such as:

  • problem solving
  • communication
  • curiosity or initiative
  • analytical thinking
  • resilience
  • learning something new

  Avoid generic answers

  Weak responses sound vague, overly broad or hypothetical (e.g., “I am very organised and work well in a team”). Strong responses:

  • are specific
  • tell a clear story
  • highlight a skill
  • show your personal contribution
  • explain what you learned

  Think of the questions as a chance to show us how you operate, not just what you’ve done.

  Show learning, not perfection

  Be honest about what went well and what you might do differently next time. We care about your ability to:

  • reflect
  • grow
  • take responsibility
  • learn from challenges

  This demonstrates maturity and potential, two things we value highly.

  Final tips

  • Stick to the word limit – 250 words per answer
  • Avoid jargon or overly complicated language
  • Make sure your answer directly addresses all parts of the question
  • Proofread for spelling and grammar

  It can sometimes be hard to write good things about ourselves, but you should be proud of and celebrate your achievements.

• CV guidance

  Your CV is your chance to give us a clear, simple snapshot of what you’ve done so far. There’s no such thing as a “perfect CV”, and everyone’s will look different so don’t worry about comparing yours to anyone else’s. What matters most is that your CV is clear, concise and easy to understand.

  What a CV should do

  A CV is evidence-based. Rather than listing traits like “hardworking” or “organised”, focus on showcasing these competencies through your experiences. Use short, outcome focused statements that highlight:

  • the task or responsibility
  • what you did well
  • the impact or result (if you can quantify this, great!)

  For example:

  “Analysed 50 survey responses and identified three key themes that informed improvements to volleyball society events.”

  Length and format

  • Keep your CV to 2 pages maximum
  • Upload it as a PDF to maintain consistent formatting
  • Use a professional file name such as: firstname_lastname_BVS_CV.pdf
  • Make sure your name and up to date contact details are clearly visible at the top

  Content tips

  • Tailor your CV to this opportunity by highlighting relevant experiences (paid, unpaid, academic or personal)
  • Include any awards, achievements or scholarships – not just academic ones
  • Consider adding links to:
    • dashboards or analysis work e.g. GitHub repositories
    • personal websites or portfolios
    • a professional, up to date LinkedIn profile
  • Use consistent formatting throughout — same font, bullet style, spacing and layout

  LinkedIn as an extra tool

  A strong LinkedIn profile can complement your CV without adding extra pages. If it is updated and professional, include the link. Useful tips:

  • Choose a clear, professional profile photo
  • Write a headline that reflects your interests (e.g. “Undergraduate aspiring to work in health data science”)
  • Use the “About” section to summarise your goals and values
  • Add detailed entries for all roles, including volunteering and student positions
  • Highlight courses, certifications, awards and relevant skills

  Common CV mistakes to avoid

  • Spelling or grammar errors
  • Not capitalising proper nouns (e.g. programme names, institutions)
  • Forgetting your name at the top
  • Missing contact details
  • Inconsistent formatting i.e. different fonts, bullet points or spacing
  • Listing experiences in the wrong order — the most recent goes first
  • Overcrowding the page with text or shrinking margins to fit more in

  Remember, assessors are reading many applications. A clear, well structured CV will help us understand who you are and what you’ve done quickly and effectively.

Frequently asked questions (FAQs)

• What is the recruitment timeline?
  • Application window opens: W/C 5 January 2026
  • Applicant information webinar: W/C 26 January 2026
  • Application window closes: 19 February 2026, 11:45 pm GMT
  • Recruitment window (shortlisting and interviews): February to March 2026
  • Intern onboarding with host organisation: April to May 2026
  • Internship: June – September 2026
• Why is the recruitment platform BeApplied used for application submission?

  We use BeApplied because it supports a fair, skills-based recruitment process. This
  approach focuses on your knowledge, motivation, and skills rather than solely on previous
  experience.

  Here’s how it works:

  • As part of your application, you will answer three sift questions on the BeApplied
    platform.
  • Your responses are then anonymised and randomised, meaning reviewers do not
    see your personal details and assess each answer independently.
  • HDR UK staff score these responses against a set scale (1–5), and each answer is
    reviewed by at least two reviewers to reduce unconscious bias.
  • CVs are only considered later by host organisations, once the initial skills-based
    assessment has taken place.

  This process helps level the playing field for all applicants and ensures that what truly
  matters—your potential—comes first. We are proud to use this fair and effective method
  across all HDR UK recruitment.

  For further information about the BeApplied platform, visit the BeApplied website.

• What support is available to help me create a strong application?

  One of our goals is that you leave the recruitment process better equipped for future
  opportunities, regardless of the outcome. While we cannot offer an internship to every
  applicant, we are committed to supporting you throughout the process and helping you
  build skills that will benefit you well beyond this programme.

  As part of this commitment, we provide the following support:

  • Applicant information webinar: We’ll share practical advice on how to craft a strong application and CV. You’ll also be able to access a downloadable Top Tips Guide on our website.
  • Feedback on your personal style statement question responses: If your application is reviewed but does not progress to the host organisation
    shortlisting stage, you will receive:
    • A spider diagram showing how your competencies (as demonstrated in your
      personal style statement questions) compare to the wider applicant pool. This will highlight the areas where you performed most strongly, as well as
      areas with the greatest scope for improvement.
    • A ranking of your responses to each personal style statement question,
      helping you understand how your answers performed relative to the
      assessment criteria.
  • Interview preparation guidance: If you are shortlisted for an interview, we’ll provide clear guidance on how to prepare effectively.
  • Feedback from host organisations: Should you reach the host organisation review stage, you will receive feedback regardless of whether you are shortlisted for interview.

  We hope these resources help you feel informed, confident, and supported at every step.

• What happens after I’ve submitted my application?

  Once you submit your application through the BeApplied platform, you will receive an automatic confirmation email. If this does not appear in your inbox, please check your junk or spam folder. If you still can’t find it, feel free to contact us at learn@hdruk.ac.uk, so we can check this for you.

  Please refer to the recruitment timeline above for the key next steps and when each stage of the process will take place.

• What should I do if I would like to withdraw my application?

  If you decide to withdraw your application prior to notification of whether you have been shortlisted for interview, you can do this via the BeApplied system. To do this, click on the ‘Resources’ tab and navigate your way down to the ‘Changed your mind?’ section where you can click the ‘withdraw application’ icon.

  If you have been shortlisted for interview but would like to withdraw, please contact learn@hdruk.ac.uk. We receive a high number of applications, and early notification allows us to offer your place or interview slot to another candidate so that no opportunity goes to waste.

• I am an international student, am I eligible to apply?

  International students may be eligible to apply, but this depends on the visa you hold and the working hour restrictions attached to it.

  Students on a Student visa (formerly Tier 4) are normally subject to term-time limits and can usually work up to 20 hours per week.

  All BVS internships are offered on a full-time basis during the summer period, so it’s important to ensure that your visa permits full-time work during this time.

  Please check the specific conditions of your individual visa, particularly regarding the definition of term time vs. vacation time for Student visa holders.

• Why are you prioritising certain applicants?

  We aim to widen access to research opportunities for individuals from backgrounds that are traditionally underrepresented in academia. Research careers often lack representation from working-class academics and those from lower socio-economic backgrounds. By prioritising these candidates, we help create a more inclusive and diverse research community.

  Below you’ll find some further information from external sources:

  • Institutional barriers hold back working class staff in colleges and universities
  • Inhabiting uncertain terrain’: inequalities of access, experience, and outcomes for university education researchers in the UK
  • Social Class and Higher Education – issues affecting decisions on participation by lower social class groups
  • Educational Inequality: Why the Education System ‘Falls Short’ for the Working Class
  • What is the social class background of academics?
• Who counts as a priority group for this programme?

  These selection of priority groups comes from guidance by the Social Mobility Commission in consultation with academic experts, think tanks, charities and employers. The full toolkit on socio-economic diversity and inclusion for employers is available online.

  Asking questions about socio-economic background (SEB) is complex, and no single questions can fully indicate a person’s SEB. This is why we have chosen to ask multiple questions with respect to the context in which we will prioritise the review of applications for individuals who identify as being from a lower socio-economic background.

  Priority groups

  First-generation university students

  You are considered a first-generation university student if neither of your parents attended university and gained a degree (e.g., BA, BSc or equivalent) by the time you were 18.

  Applicants who meet this definition will be recognised as being from a priority group, as they may have faced additional barriers accessing higher education.

  Recipient of free meals at school

  You fall into this category if you were eligible for Free School Meals at any point during your school years.

  Answering “yes” to this question means you will be classified as being from a priority group, as this is a widely used indicator of socio-economic disadvantage.

  The occupation of your main household earner at aged 14

  This refers to the job held by the person who contributed the most to your household income when you were 14. Certain types of occupations are strongly linked to levels of socio-economic opportunity and access.

  Lower socio-economic occupations include:

  • Technical and craft occupations
  • Routine and semi routine manual or service occupations
  • Long-term unemployment

  Selecting one of these categories during the application process means you will be recognised as being from a priority group, helping us identify and support applicants who may have grown up facing structural barriers.

  Care experienced

  You are considered care-experienced if you have spent any time living:

  • With foster carers under local authority care
  • Care-experienced individuals will be classified as being from a priority group, recognising the unique challenges they may have faced.
  • In residential care (e.g., children’s homes)
  • At home under a supervision order
  • In kinship care with relatives or family friends, either formally (e.g., a special guardianship order) or informally without local authority involvement

  Care-experienced individuals will be classified as being from a priority group, recognising the unique challenges they may have faced.

   

  We also aim to recruit students from non-Russell Group universities. Therefore, if you meet one or more of the priority areas above and are studying at a non-Russel group university, your application will be prioritised event further.

  List of Russel Group universities

• Does this mean others can’t apply?

  No. Anyone can apply. However, if you meet the eligibility criteria, score at or above the pass mark and belong to one of these priority groups, your application will be prioritised for review by the host organisation.

• How does the scoring process work?
  • All applications are assessed against the same criteria.
  • We set a pass mark after reviewing all scores for each project to ensure fairness.
  • Eligible candidates below the pass mark will not progress.
  • Among those who meet or exceed the pass mark, priority group applicants are prioritised for referral to host organisations for shortlisting.
• Will my diversity information be shared?

  No. Your diversity information is confidential and will not be shared with host organisations. We will, however, share some diversity information with Wellcome upon request for reporting purposes. This will be aggregated and non-identifiable. Please refer to the BVS webpage for the full data protection statement.