• San Francisco , CA
  • Information Technology
  • Full-Time
  • 279 Bridgeview Way

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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 Environment Just north of Silicon Valley the lab location at UCSF Mission Bay directly adjoins SoMa district and the heart of SF 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 the application email listed Please reference ldquo;postdoc dnn md rdquo;

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
Associated topics: backend, c/c++, design, devops, expert, java, perl, php, programming, senior

* The salary listed in the header is an estimate based on salary data for similar jobs in the same area. Salary or compensation data found in the job description is accurate.

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