We focus on the development and understanding of large multimodal models (such as MAIRA) for problems in healthcare and biomedical discovery.

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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

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‍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

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‍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
  
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‍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

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