Post-doctorate fellowship in machine learning for medical imaging and bioinformatics

Institution/Company:
University of British Columbia
Location:
Vancouver, BC, Canada
Job Type:
  • Postdoctoral
Degree Level Required:
PhD
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Post-doctorate fellowship in machine learning for medical imaging and bioinformatics

A post-doctoral fellowships (PDF) position in the field of machine learning/computational biology is available in the School of Biomedical Engineering and Department of Pathology and Laboratory Medicine at the University of British Columbia/Vancouver General Hospital. The incumbents will join an interdisciplinary team working to establish a program on machine learning applications in pathology and genomics. Candidates are expected to have extensive experience in one or more of the following areas: diverse areas of machine learning and statistics especially deep neural networks, statistics, and image processing. The candidate will play an important role in the project team, including authoring grants, conducting large-scale international projects, managing collaborations, disseminating research findings, and assisting in managing junior lab members.

Organizational Status: The successful candidate will report to the Principal Investigator (Director of AI Research at OVCARE, Dr. Ali Bashashati).

Responsibilities:

• Developing efficient deep learning algorithms for the classification and risk-stratification of patients based on whole slide tissue images. • Developing machine learning algorithms for studying the correlation between genomic/transcriptomic signatures and whole slide tissue images in cancer samples. • Senior members: participation in the preparation of manuscripts and grant applications. • Senior members: Participation in supervising junior lab members.

Qualifications:

• Degree in computer science, bioinformatics, biomedical engineering, electrical engineering, or similar. • Proven experience in the design and implementation of deep learning algorithms in image processing. • Outstanding programming skills in Python and R. • At least 2 years of experience working on one or more of the following areas: image processing, machine learning, large-scale genomics/transcriptomics, single-cell sequencing. • Track record of development and implementation of novel machine learning algorithms in cancer genomics. • Extensive experience in utilizing machine learning libraries such as TensorFlow, Keras, PyTorch, and Scikit-learn. • At least 2 years of experience working with Linux computing clusters. • Ability to work independently and within a team environment. • Effective oral and written communication, analytical, and interpersonal skills. • Outstanding publication record

  • How to Apply

    • Please send applications to maryam.asadi@ubc.ca. In the body of your email, please mention your earliest availability date. • Your package should include detailed CV, transcripts.