Location: San Francisco, CA
Institution: University of California San Francisco
The Keiser lab at UCSF (keiserlab.org) is looking for highly motivated postdoctoral candidates with a background in machine learning, systems pharmacology, computational chemistry, bioinformatics, or related fields. The candidate would work to integrate deep learning with pharmacology. The project involves the design of deep neural networks for the prediction of small molecule binding activities and their role in phenotypic screens.
Desired, but not strictly required, skills include experience with theano, lasagne/keras, TensorFlow, pandas, rdkit, and sklearn. Expertise with CUDA and/or massive dataset analysis (e.g., NoSQL, AWS, Google Cloud) is a plus. A productive track record with at least one major first-author publication is required. We seek a driven individual who will lead his/her 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 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 to firstname.lastname@example.org. Please reference “postdoc-dnn”.
UCSF is an equal opportunity employer.