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Date: Tue Feb 5 18:37:39 2019
Subject: 19.02.05 Postdoc - Deep Learning and Molecular Dynamics - UCSF, 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 labs location at UCSF Mission Bay 
directly adjoins SoMa district and the heart of SFs 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^-^ Please reference 

UCSF is an equal opportunity employer.

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Modified: Tue Feb 5 23:37:39 2019 GMT
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