Machine Learning Postdoc at Harvard Medical School - Sander Lab

[Prash]

Dear Colleague: 

perhaps you can forward this to interested recent or imminent PhDs, who may be interested in joining my research group at Harvard Medical School.

Systems Biology at Harvard Medical School - Postdoc position

There is an opportunity for postdocs in data science and machine learning to solve challenging problems: 

(1) Catch cancer early using AI

(2) Develop large-scale predictive models of cell biology

(3) Design proteins for environmental or therapeutic purposes

July 15, 2024————————————————————————————————————

The ad:

Machine Learning Postdoc at Harvard Medical School

The lab of Chris Sander, in collaboration with Debora Marks, plans to recruit postdocs to work on AI for cancer risk prediction, computational cell biology, and protein design. Join us to develop and apply machine learning and statistical physics methods for impactful research in human disease and synthetic biology.

We are in the department of systems biology and collaborate with research groups in the Boston area, including the Ludwig Center, Mass General Hospital and the Broad Institute, and with researchers in the US, Canada, Denmark, Germany, China, and the UK.

Areas of focus:

• Cancer Risk Prediction: Use machine learning to identify patients at high risk for aggressive cancers so they can be enrolled in interception programs for prevention, early detection and early-stage treatment. On github: CancerRiskNet. 

• Perturbation Biology: Develop computational models of cell biology from large-scale experiments - link novel perturbations with molecular and phenotypic changes, so as to guide therapeutic developments and cell biological experiments. On github: CellBox, scPerturb.

• Protein Function and Design: Predict protein function from sequences, design novel proteins for environmental or therapeutic purposes, and collaborate on engineering beneficial gene and protein modules.  See for example: bit.ly/betalacdesign

Qualification::

• PhD in biology, medicine, mathematics, computer science, physics, chemistry, or engineering.

Application:

• Send CV, bibliography, statement of research interest (~1 page), and names of 3 references to sander.research #at& gmail.com. Join us for basic and applied research in biological machine learning and data science in Boston, with collaborative international connections.

Sander lab:

Armenise Building, Systems Biology, Harvard Medical School

List of publications via Google Scholar 
By year of publication:  http://bit.ly/ACCayl 
By citation count: http://bit.ly/yAdPhU

Key publications:

1 First successful folding and all-atom 3D structures from evolutionary couplings just from sequence information - http://bit.ly/tob48p
2 cBioPortal for Cancer Genomics -  knowledge tool for cancer research - DOI: 10.1158/2159-8290.CD-12-0095
3 Protein structure from experimental evolution - bit.ly/3DseqOpen
4 Pancreatic cancer risk predicted from disease trajectories using deep learning – bit.ly/pdacrisk-natmed 

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