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Postdoctoral Fellowship, Geometry & Deep Learning for 3D Biological Shape Reconstructions

University of California Santa Barbara - BioShape Lab

Location: Santa Barbara - United States
Salary: Not Specified
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 8th October 2021
Closes: 1st December 2021

Postdoctoral Fellowship

Geometry & Deep Learning for 3D Biological Shape Reconstructions: Reveal Membrane Proteins with Cryo-Electron Microscopy

Contact

Nina Miolane, UCSB (ninamiolane@ucsb.edu)

What We Offer You

As a member of the BioShape Lab, you will have the opportunity to explore theoretical foundations of geometry and machine learning research, and to develop cutting-edge practical methods for biology and medicine. You will also receive guidance and directions tailored to your career goals, while being offered advice to refine such goals. You will find an exciting working environment fostering collaborative brainstorming, creative thinking, technical implementations, and practical applications of the most innovative ideas. If you are driven by excellence, without compromising on quality of life, you will fit right in! 

Our lab is embedded in the thriving community of the University of California, in the Santa Barbara campus located at a fabulous ocean location a few hours drive from San Francisco, the Silicon Valley and Los Angeles. We work in collaboration with clinicians and experts in molecular and cell biology, such as researchers from Stanford SLAC and USC, to advance the frontiers of biomedical knowledge and AI-assisted medical practice world-wide. This provides an exciting environment to tackle scientifically and socially relevant challenges in AI applied to biology and medicine. Other advantages include: help for relocation to California, flexible working hours, career advice and international travels. 

Applications accepted until the position is filled. Apply before December 1st, 2021 to be given full consideration. Start date flexible. Applicants are invited to fill this form: here.

Scope

While the development of cryo-electron microscopy (cryo-EM) has already proven to revolutionize the field of structural biology by imaging biomolecules in solution, the vast majority of proteins cannot be reconstructed at a satisfying resolution. Among them, membrane proteins still remain an immense imaging challenge for biologists. Meanwhile, membrane proteins are prominent targets for over 50% of prescription drugs on the pharmaceutical market, including drugs targeting the treatment of neurological disorders and cancers. In this context, the technological limitations of cryo-EM imaging restricts our understanding of the proteins’ 3D conformations, and consequently, our knowledge of the associated therapeutic mechanisms. 

The focus of this project is to develop new geometric deep learning methods that enhance the resolution of cryo-EM reconstructions, particularly targeting membrane proteins. The successful candidate will revisit the paradigm of 3D shape reconstruction using tools for unsupervised geometric deep learning, such as geometric (variational) autoencoders and/or generative adversarial network. 

We seek candidates whose interests are related to deep learning, geometry, and 3D shape analysis and with the following qualifications:

  • Ph.D. in fields related to applied mathematics, computer science, machine learning, etc.
  • Experience in Python, machine learning
  • Ability to lead independent research.

Team

The successful candidate will be primarily located in Santa Barbara, California and will be part of an international team of collaborators, working with Nina Miolane (Electrical and Computer Engineering, UCSB), Cornelius Gati (Structural Biology, USC), Khanh Dao Duc (Mathematics, University of British Columbia, Vancouver), Frédéric Poitevin (Biological Imaging, Stanford SLAC) and Claire Donnat (Statistics, University of Chicago).

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