Computational methods and AI models for medical image analysis: Generative AI, Vision-Language Model, Multimodal Learning and Foundation Model

About the Project

Are you a prospective PhD student looking to make a significant impact in healthcare AI? Join our dynamic research team as we delve into the transformative potential of cutting-edge technologies like generative models and foundation models.

In this project, we're committed to developing AI models that not only tackle the complexities of medical imaging data but also bridge the domain and knowledge gaps across diverse scenarios. Our goal is to create robust and adaptive algorithms capable of providing accurate diagnoses, personalised treatment plans, and ultimately, improved patient outcomes.

Depending on your expertise and interests, you'll have the opportunity to specialise in developing reliable machine learning models using foundation models, generative techniques, and/or multimodal learning approaches. Whether you're passionate about advancing the foundations of AI or exploring the creative potential of generative models, this project offers a unique chance to contribute to the future of healthcare.

Join us in revolutionising medicine through innovative applications of AI technologies. If you're ready to tackle real-world challenges and drive meaningful change, we want to hear from you. Apply now and be part of a groundbreaking journey towards enhancing the reliability and applicability of AI algorithms in healthcare.

Project Overview

This PhD project aims to develop advanced AI technologies that improve healthcare—enhancing clinical workflows, supporting diagnosis, and ultimately improving patient outcomes. The research will focus on designing deep learning models that are not only accurate and innovative, but also robust, explainable, and suitable for deployment in real clinical environments.

You will explore state-of-the-art methods in:

• Multimodal learning
• Generative models
• Foundation models
• Digital twins
• Vision–language models (VLMs)
• Medical Large language models (LLMs)

These techniques will be applied to diverse medical data types, including medical imaging, clinical text, and electronic health records.

Research Environment

You will join the Digital Healthcare and Medical Imaging Research Group. The group conducts interdisciplinary research at the intersection of artificial intelligence, medical imaging, and digital health technologies.

We collaborate closely with clinicians, healthcare providers, and academic researchers to ensure our work translates into real-world clinical benefit. Our research involves a wide range of medical imaging modalities, including ultrasound, MRI, CT, microscopy, OCT, and more.

Collaborations

Our team members and collaborators include researchers from:

• University Hospital Birmingham
• Johns Hopkins University
• Queen Mary University of London
• University of Oxford
• University College London

These partnerships provide access to diverse expertise, datasets, and international research opportunities.

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.

Funding Notes

4 years studentship covering UK home fees or International student fees.