What We are Looking For

Find out what we expect successful applicants to bring to the programme.

We encourage applicants to provide evidence of the qualities below in their application, through coursework, projects, work experience, or independent learning. You do not need direct experience with in every area, but you should show motivation to learn, awareness of why these skills matter, and a thoughtful attitude toward developing them during the programme. 

We recognise that applicants come from many different starting points. If you have taken a non-traditional route or faced circumstances affecting your performance, we encourage you to describe this in your application so we can consider it appropriately.

General Advice

  • Please provide specific support for statements you make in your application. For example, if you have been developing your knowledge of a topic you should describe how. If you can refer directly to sources that provide more detail, then you should do so. You could include examples from coursework, projects, or work experience. Tell us what you did, what you learned, and what experience you gained that is relevant to our programme. This allows us to give due weight to your accomplishments, get a holistic view of you, and helps us to gauge your potential as a prospective PhD student.
  • We do not expect you to have direct experience of all areas. Our programme is designed to enable people from different backgrounds to fill gaps in their knowledge and become successful researchers in biomedical AI innovation.
  • We look for a strong motivation to learn, an awareness of why you need skills across several disciplines, and a thoughtful attitude toward developing yourself throughout the programme.

Strong foundation in one core area + commitment to build expertise in the others

The standard qualification requirement for entry to the CDT is a UK 2:1 honours degree, or its international equivalent, in an area related to the topic of the CDT. This can include computer science, AI, cognitive science, mathematics, physics, engineering, biomedical science, biological science, and clinical & public health sciences. 

You do not have to be skilled in both AI/ML and biomedicine/health, but you need to show genuine effort to grow in the complementary field.

If you come from a life sciences background

If your background is in life sciences, you should demonstrate that you have already taken steps toward developing quantitative and computational skills. This could include skills training in programming, data analysis, or computational thinking, and ideally evidence of successful deployment of these skills in the form of a project (even small efforts can demonstrate your commitment).

Examples include:

  • A small coding or data-analysis project (self-directed, academic, or lab-based)
  • Python / R coursework, summer schools, hackathons, possibly with online certificates
  • Statistics or data analysis in your coursework or job duties
  • A GitHub repository, Kaggle challenge, or something similar
  • Reading reviews, research articles, blogs, and news items to familiarise yourself about the methodologies used and current challenges in applying AI in biomedicine, clinical, and health areas.

If you come from a computing/engineering background

If you come from a computing or technical background, you should demonstrate genuine interest in biomedical or health applications and awareness of the challenges specific to this sector. It is important to demonstrate motivation for applying your technical skills responsibly in a health context.

Examples may include:

  • Research projects related to biology, medicine or health data
  • Coursework, short courses, or self-directed study in biology/health
  • Reading and referencing biomedical literature and emerging challenges in your personal statement
  • Participation in healthcare hackathons/competitions
  • Interest in ethical, regulatory or societal issues in medical AI

Interest in responsible and ethical AI for health

We value applicants who appreciate that AI in medicine carries ethical, societal, and patient-safety responsibilities. You should demonstrate curiosity for issues associated with fairness, transparency, privacy, and real-world impact, and show openness to developing thoughtful and responsible research practices.

Collaborative and open mindset

The CDT provides a highly interdisciplinary research environment. As part of your training, you will collaborate with clinicians, researchers of complementary backgrounds, and external partners, so openness, respect, and teamwork are essential. 

You should demonstrate readiness for learning from and supporting your peers, as well as your qualities for contributing to a positive and inclusive cohort culture. 

Ideally, your application should provide evidence, through specific examples from your past experiences, for your ability to thrive in and contribute to this environment

Willingness to develop a broad skillset

You should be ready to engage with technical, biomedical, and professional skills, from coding and statistics to reproducibility, communication, and domain understanding.

Enthusiasm for engaging public and patient communities

Societal trust in acceptance of AI methods is essential to bring about real innovation in biomedical applications. We encourage students to communicate their work clearly and involve patient and public voices in shaping research. 

You should be motivated to help build trust, understanding, and dialogue around AI in healthcare through outreach, communication, and involvement activities.

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