Sander Lab in Systems Biology at Harvard Medical School - Postdoc position
There is an opportunity for two postdocs in data science and machine learning to solve challenging problems:
(1) Catch cancer early using AI
(2) Develop large-scale models of cell biology
We're looking for researchers who have a track record in machine learning and computational biology, and a strong desire to solve challenging biological problems. We focus on catching cancer early, by defining high risk populations via AI training on clinical trajectories. And we are developing predictive full-scale models of cell biology, so we can design interventions to cure disease. Your work can make a real difference in the lives of others.
(1) Catch cancer early: cancer risk prediction
We use time series machine learning algorithms to learn from millions of clinical records which patients are at high risk for aggressive cancers. In collaboration with clinicians, the highest risk patients can be enrolled in surveillance programs with the aim to catch cancer as early as possible. Involves close collaboration with the Brunak group in Copenhagen, the Fillmore group in the US Veterans Administration, the UK Biobank and others.
(2) Make cell biology predictive: cell state dynamics via perturbation biology
From large-scale perturbation experiments and molecular and phenotypic profiles, develop computational models of cell biology that couple perturbations to molecular and phenotypic changes. Use the cell state dynamics models to develop novel combinations of perturbations with desired therapeutic effects and to guide experiment across many areas of cell biology. Involves collaboration with the Blüthgen group in Berlin, the Mann lab in Copenhagen, as well as with labs in the Wellcome Leap Delta Tissue network, including the Marks Lab at HMS and the Schumacher group in the Centre for Regenerative Medicine in Edinburgh.
You also have the opportunity to engage in a scientific network with interests to:
- Predict the future evolution of viruses and design combinatorial vaccines.
- Design environmentally useful proteins, learn protein function from sequences, fold proteins from single sequences.
- Make biological knowledge computable and create knowledge bases, such as the cBioPortal for cancer genomics and Pathway Commons.
- Predictive dynamics of future evolution of DNA-based and technology-based living systems.
PhD or equivalent in quantitative sciences
If you are ready to make a real impact on the future of biomedicine and synthetic biology, we encourage you to apply, preferably by March 30, 2023. Send brief statement of research interests, CV, three references (names or letters), US residency/visa status requirements to sander-dot-research-at-gmail-dot-com (subject: postdoc application). Together, we can unlock the potential of machine learning to solve hard biological problems and improve lives.
Harvard Medical School is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, national origin, disability status, veteran status, gender identity, sexual orientation, pregnancy or any other characteristic protected by law.