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Postdoc @ UCSF - Deep Learning and Biomedical Pattern RecognitionPosted 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, systems pharmacology, bio/medical image analysis, drug discovery, pattern recognition, or related fields. The candidate would define or join a deep learning research project compatible with lab directions in biological or medical image or video analysis, drug discovery, chemical screen mechanism of action discovery, time series analysis, or by building on ongoing clinical collaborations. Broad themes across these application domains include model interpretability and representation learning.
Python expertise required. Desired, but not strictly required, skills include experience with PyTorch, Chainer, pandas, and sklearn. Expertise with containers (e.g., NGC, singularity), AI-ops (e.g., CI/CD for ML), rapid caching, and/or distributed dataset/model analysis is a plus.
A productive track record with at least one first-author publication is required. We seek a driven individual who will lead her/his research independently and communicate frequently and clearly to the field.
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 applykeiserlab.org. Please reference "postdoc-dnn-pattern".