Details
A 3.5-year funded PhD studentship is available in the Department of Computer Science at University of Sheffield, working on the topic of multi-modal AI for healthcare.
In recent years, artificial intelligence (AI) technologies, such as foundation models, multi-modal learning algorithms, generative models, and large language models [1], have made seen remarkable advancements in the field of healthcare. It can lead to more accurate diagnosis, personalized treatment planning, and improved patient outcomes. This project will focus on developing multi-modal AI models that can handle the complexities of medical data as well as the domain gap and knowledge gap across different scenarios, and further adapt to individual patient needs. Depending on the profile of the student, a particular focus would be utilizing large language models and explainable AI (XAI) to enhance the reliability and applicability of AI algorithms for healthcare applications [2] and/or identifying pitfalls of current AI models using adversarial machine learning.
Supervisor Bio
Dr Chen (Cherise) Chen is a Lecturer in the Department of Computer Science at University of Sheffield. She was a post doc at the University of Oxford after she obtained her Ph.D. from the Department of Computing at Imperial College London in 2022.
Personal website：https://cherise215.github.io/
About the Department/Research Group
The successful candidate will join the Computer Vision group in the Department of Computer Science, led by Prof. Jungong Han. As a PhD candidate, you will have access to state-of-the-art facilities, extensive resources, AI-focused community (https://shef-ai.github.io/) and a collaborative research ecosystem, incl. partnerships with Insigneo —the largest institute in Europe dedicated to in silico medicine technologies (www.sheffield.ac.uk/insigneo).
Candidate Requirements
- Being self-motivated and enthusiastic about doing research in AI for healthcare and a commitment to supporting high quality research.
- Have a good first degree in computer science, statistics, physics, engineering, or any-related field.
- Experiences with AI for healthcare related projects using PyTorch and/or TensorFlow libraries.
- Strong programming skills such as Python, C++, C, Java are preferred.
- Excellent oral and written communication skills.
- Strong problem-solving abilities.
- Experiences of presenting or preparing scientific manuscripts in journals or conferences is preferred.
- If English is not your first language, you must have an IELTS score of 6.5 overall, with no less than 6.0 in each component.
How to Apply
To apply for a PhD studentship, applications must be made directly to the University of Sheffield using the Postgraduate Online Application Form. Make sure you name Dr Chen Chen as your proposed supervisor.
Information on what documents are required and a link to the application form can be found here -
https://www.sheffield.ac.uk/postgraduate/phd/apply/applying
The form has comprehensive instructions for you to follow, and pop-up help is available.
Your research proposal should:
- be no longer than 2 A4 pages + unlimited references
- outline your reasons for applying for this studentship
- explain how you would approach the research, including details of your skills and experience in the topic area
Enquiries including a letter of interest, an up-to-date CV and transcripts should also be sent to Dr Chen Chen ([email protected]) beforehand. Please add quote [PHD-AI4Health] in the email subject line.
Funding Notes
The PhD studentship will cover standard UK home tuition fees and provide a tax-free stipend at the standard UK Research Council rate (currently £19,237 for the 2024/25 academic year) for 3.5 years. If you are an overseas student, you are eligible to apply but you must have the means to pay the difference between the UK and overseas tuition fees by securing additional funding or self-funding. Further information on International fees can be found here: https://www.sheffield.ac.uk/new-students/tuition-fees/fees-lookup
References
[1] Moor, M, et al. Foundation models for generalist medical artificial intelligence. Nature, 2023
[2] Chen, Chen, et al. "Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction." 2024