Microsoft Research Health Futures UK conducts world-class research at the intersection of AI and healthcare, drawing on deep expertise in biomedical imaging and multimodal deep learning. We are seeking research interns to join our vibrant team of researchers and engineers.
The role
We focus on the development and understanding of large multimodal models for healthcare, as exemplified by our work in Project MAIRA. As an intern within our team, you will have the opportunity to:
- Deepen your expertise in multimodal deep learning and contribute to our ambitious research agenda
- Conduct experimentation with world-class computational resources
- Learn in a team with a strong culture of collaboration and rigorous research
Responsibilities
The ideal candidate will have strong intellectual curiosity and passion to solve real-world problems in healthcare using machine learning. Responsibilities will include:
- Co-development of an internship project in collaboration with the supervisor
- Design, implementation and evaluation of new machine learning methods and models
- Presentation and communication of research findings
Qualifications
Required/Minimum Qualifications:
- Currently enrolled in a PhD or master’s program in areas such as computer science (e.g. machine learning, deep learning, signal processing), computational biology, medicine
- Prior experience with deep learning frameworks (e.g., PyTorch) and some familiarity with software engineering practices (e.g. git)
- Passion for healthcare and medicine
- Ability to work and learn in a collaborative in a diverse environment
Other requirements
Preferred/Additional Qualifications:
- Expertise in or enthusiasm for any of the following topics:
- Representation learning including self-supervised learning, multimodal learning
- Interpretability methods for deep learning (e.g. mechanistic interpretability, explainable AI)
- Design, training, or evaluation of large multimodal transformers
- Biomedical imaging such as radiology, computational pathology
- Biomarker discovery (broadly interpreted)
- Large language models for healthcare and medicine
- Experience with real-world healthcare data
- Track record of publication in conferences or journals within machine learning and/or healthcare
