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Ensemble machine learning methods for biomedical imaging


Project Description

Various projects are available in the Arnaout Laboratory at UCSF. The Arnaout laboratory studies deep and machine learning for biomedical imaging and related clinical data, with the goals of decreasing diagnostic error and developing and scaling novel phenotypes to drive precision medicine research. UCSF is a top-10 medical center and a leader in cross-campus efforts to mine, harmonize, and analyze multi-modal clinical data for the University of California’s 15 million patients.

The Arnaout laboratory is part of both the Bakar Computational Health Sciences Institute, where the abovementioned efforts are based, and the nationally ranked Department of Medicine. Projects focus on deep learning for medical imaging, and through collaborative work with intra- and inter-institutional partners, touch the electronic health record, genetics, and other sources of data.

Please see for more details.


Probably yes. Funding is available, depending on skills/training level.


Required Skills
  • Python
  • SQL
  • Git/Github
  • HPC/GPU computing
  • TensorFlow
  • PyTorch
  • MLlib / machine learning tools
  • Natural Language Processing tools (NLTK, CTAKES, PyTorch-NLP, etc)
Required Course Work or Level of Knowledge
  • Statistics, intermediate
  • Machine Learning, introductory - advanced
Acceptable Level of Education (eg. Undergrad, Grad Students, Post Docs, MD, PhD)

Undergraduate students, Graduate students, Postgraduates, Full-time workers who volunteer time


PI/Research group

Arnaout Laboratory


Rima Arnaout, [email protected]