PhD Studentship: Computational Methods for Medical Image Analysis: Foundation Models, Generative Models and Multimodal Learning
4 weeks ago
The field of medical imaging and precision medicine has seen remarkable advancements in recent years, driven by the potential of artificial intelligence (AI) technologies, such as generative models, foundation models, multi-modal learning algorithms, and large language models. These technologies can revolutionize healthcare by enabling accurate diagnosis, personalized treatment planning, and improved patient outcomes. This project will focus on developing robust and adaptive AI models that can handle the complexities of medical imaging 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 developing reliable machine learning models with Foundation models, Generative machine learning techniques and/or Multimodal Learning to enhance the reliability and applicability of AI algorithms for healthcare applications.
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.
For more details about this position and application, please contact Dr. Le Zhang:
Information on the English language requirements can be found here:
Funding notes:
3.5 years studentship covering UK home fees only (overseas applicants are welcome to apply, but they must be able to fund the difference between home fees and overseas fees themselves/through additional funding).
References:
[1] Moor, M, et al. Foundation models for generalist medical artificial intelligence. Nature, 2023
[2] Zhou, Y., Chia, M.A., Wagner, S.K. et al. A foundation model for generalizable disease detection from retinal images. Nature 622, 156–
[3] Müller-Franzes, G., Niehues, J.M., Khader, F. et al. A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis. Sci Rep 13,
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