Microsoft Research Health Futures UK conducts research at the interface of machine learning, healthcare, and life sciences. Drawing on our expertise in biomedical imaging and multimodal deep learning, we are part of global team of scientists and engineers advancing human health through research.

We are seeking a motivated researcher to join our team based in Cambridge, UK. We focus on methodological and applied research informed by real-world applications in healthcare and life sciences research. As a postdoctoral researcher within the team, you will sit within one of our two main pillars:

• Medical imaging, including inverse problems and image interpretation via deep learning.

• Multimodal computational biology for scientific discovery.

You will have the opportunity to:

• Deepen your expertise by developing an ambitious research agenda aligned with the team’s work

• Conduct experimentation with world-class computational resources

• Grow in a team with a strong culture of collaboration and rigorous research.

The ideal candidate will have strong intellectual curiosity and passion to solve real-world problems through deep research and will align with one of our two research pillars. This position is a part of our two-year residency program and provides a unique opportunity to conduct groundbreaking research.

• Design, implement and evaluate new machine learning methods and models

• Share your research with the broader research community through publication or open sourcing

• Collaborate with clinical experts and across Microsoft Research and Health Futures

• Work as part of a team to advance our ambitious research agenda

Required/Minimum Qualifications:

• PhD degree (or on-track to complete), or equivalent expertise in independent research, 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 software engineering practices (e.g. git)

• Passion for healthcare, medicine, or life sciences research

• Ability to work and learn in a collaborative and diverse environment

Preferred/Additional Qualifications:

• Expertise in multimodal and large-scale deep learning, such as model design, training, and evaluation

• Experience with real-world healthcare or life-sciences data

• Expertise in one of the following areas:

• • Medical imaging, in particular MRI, OR:

• • Machine learning for scientific discovery in computational biology and medicine, in particular:

• • • Data modalities such as digital pathology, transcriptomics, single-cell technology, multimodal integration

• • • Interpretability methods for deep learning (e.g. mechanistic interpretability, explainable AI)

• • • AI for scientific discovery broadly interpreted

• Track record of publication in conferences or journals, such as: NeurIPS, ICML, ICLR, CVPR, ML4H, MICCAI, IPMI, ISMRM, EMNLP, Nature (and affiliated, e.g. Nature Medicine, Communications)