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Postdoc @ UCSF - Deep Learning and Molecular Dynamics

Posted by: Keiser Lab at UCSF

Posted date: Feb-05-2019

Location: San Francisco, CA

The Keiser Lab at UCSF is looking for highly motivated postdoctoral candidates with a background in machine learning, molecular dynamics, computational chemistry, or related fields. The candidate would work to integrate deep learning methods with molecular dynamics (MD) simulations. The project involves the design and testing of efficient computational ligand-protein interaction representations for the analysis of MD trajectories using convolutional neural networks.

Qualifications

Python expertise required. PyTorch or Chainer experience preferred. Desired, but not strictly required, skills include experience with pandas and sklearn. Expertise with massive and/or distributed dataset analysis is a plus. MD trajectories will be provided by expert partners and the project will proceed under an established collaboration.

A productive track record with at least one first-author publication is required. We seek a driven individual who will hit the ground running, lead her/his research independently, and communicate frequently and clearly to the field and industry partners.

Environment

Just north of Silicon Valley, the lab’s location at UCSF Mission Bay directly adjoins SoMa district and the heart of SF’s tech and artificial intelligence startup scene.

How to apply

Interested candidates should submit a CV and arrange that three letters of reference be sent directly to apply(at)keiserlab.org. Please reference “postdoc-dnn-md”.
Job Title Postdoc (at) UCSF - Deep Learning and Molecular Dynamics
Post Details
Email apply(at)keiserlab.org
Employer's Website www.keiserlab.org
Category
Job Discipline Job Discipline -> Computational Biology
Job Classification Job Classification -> Postdoctoral Researcher
Job Type Job Type -> Full-time
Location San Francisco, CA
Key Words machine learning, deep learning, molecular dynamics, computational chemistry
Start Date 2019/03/01
Deadline