Postdoctoral Fellow in Cardiovascular Genetics and Deep Learning
Join the Pirruccello Lab at UCSF to advance cardiovascular research using deep learning and genetic epidemiology.
Best for:Best for postdoctoral researchers with a PhD in biomedical engineering, genetics, epidemiology, or computer science, skilled in deep learning, statistical genetics, and large-scale data analysis.
Overview
This postdoctoral position at the University of California, San Francisco (UCSF), within the Pirruccello Lab, focuses on cutting-edge research at the intersection of cardiovascular genetics, deep learning, and epidemiology. The fellow will develop and refine cardiac and aortic valve phenotypes using computer vision and deep learning tools, analyze large-scale biobank datasets (including UK Biobank), and investigate genetic and epidemiological relationships. The role emphasizes independent research, extramural funding acquisition, and high-impact publication dissemination. Professional development opportunities include cross-disciplinary seminars and mentorship in deep learning and genetics. The position is ideal for researchers with a PhD in a relevant field and expertise in statistical genetics, deep learning, and bioinformatics.
What you'll do
- Develop and refine cardiac and aortic valve phenotypes using computer vision and deep learning tools on large-scale biobank datasets (e.g., UK Biobank and UCSF imaging studies).
- Investigate genetic and epidemiological relationships between phenotypes and cardiovascular traits using statistical genetics and deep learning methods.
- Translate research findings into ECG-based, proteomic, and classical statistical models.
- Independently design experiments, analyze complex datasets, and apply for extramural funding to support research initiatives.
- Disseminate findings through high-impact publications and presentations at national and international scientific meetings.
- Participate in cross-disciplinary seminars and gain expertise in deep learning, genetics, and professional development opportunities.
- Collaborate with interdisciplinary teams to advance cardiovascular research and improve understanding of aortic valve hemodynamics.
Requirements
Degree required: PhD in biomedical engineering, genetics, epidemiology, computer science, bioinformatics, or a related quantitative field
Language: English; proficiency required for communication and publication
Instruction: English
Publications: Expected to produce relevant publications in peer-reviewed academic journals or conferences.
- Proven expertise in deep learning and computer vision, particularly for medical imaging analysis.
- Strong background in statistical genetics, including common- and rare-variant analysis.
- Experience with large-scale biobank data analysis and bioinformatics tools.
- Ability to independently design experiments and analyze complex datasets.
- Familiarity with ECG-based, proteomic, and classical statistical modeling techniques.
- Must hold a PhD in a relevant field (biomedical engineering, genetics, epidemiology, computer science, bioinformatics, or related).
- No clinical duties or patient contact permitted.
Funding & terms
How to apply
Deadline: 2026-11-21
Applicants should submit their CV and any supplementary materials via email to James Pirruccello at James.Pirruccello@ucsf.edu. Ensure your application highlights your relevant research experience and skills in deep learning, genetics, and bioinformatics.
- CV
- Supplementary application materials (if any)
Contact
James Pirruccello· MD
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