We focus on the development and understanding of large multimodal models (such as MAIRA) for problems in healthcare and biomedical discovery.
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 program in areas such as computer science (e.g. machine learning, deep learning, signal processing), medical imaging, 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
- Experience with real-world healthcare data.
- Ability to work and learn in a collaborative and diverse environment
Preferred/Additional Qualifications:
- Representation learning self-supervised learning, unimodal or multimodal learning
- Expertise in or enthusiasm for any of the following topics:
- Interpretability methods for deep learning (e.g. mechanistic interpretability, intrinsically interpretable methods, representation engineering, circuit discovery or rule extraction)
- Design, training, or evaluation of large unimodal or multimodal transformers
- Biomedical imaging such as radiology, computational histopathology
- Computational biology including -omics, bioinformatics, when coupled with deep learning
- Clinical data integration or multimodal fusion
- Large language models for healthcare and medicine, biomedical natural language processing, post-training of LLMs/RLAIF
- AI for scientific discovery, including hypothesis generation, biomarker discovery
- Causal machine learning
- Track record of publication in conferences or journals within machine learning and/or healthcare
