Machine Learning Scientist
Job Description
OUR VISION
Our vision is to be the nation’s leading public school of medicine. We are ranked 2nd in primary care education among all US schools of medicine and 5th among public peers in NIH research funding. Our Allied Health Department is home to five top-ranked divisions, and we are home to 18 top-ranked clinical and basic science departments in NIH research funding.
OUR MISSION
Our mission is to improve the health and well-being of North Carolinians and others whom we serve. We accomplish this by providing leadership and excellence in the interrelated areas of patient care, education, and research.
Patient Care: We will promote health and provide superb clinical care while maintaining our strong tradition of reaching underserved populations and reducing health disparities across North Carolina and beyond.
Education: We will prepare tomorrow’s health care professionals and biomedical researchers by facilitating learning within innovative curricula and team-oriented interprofessional education. We will cultivate outstanding teaching and research faculty, and we will recruit outstanding students and trainees from highly diverse backgrounds to create a socially responsible, highly skilled workforce.
Research: We will develop and support a rich array of outstanding health sciences research programs, centers, and resources. We will provide infrastructure and opportunities for collaboration among disciplines throughout and beyond our University to support outstanding research. We will foster programs in the areas of basic, translational, mechanistic, and population research.
UNC Global Women’s Health (GWH) is a unique group of clinicians, researchers and public health professionals working to improve the health of women and children in the world’s poorest countries. Driven by outcomes and intensely practical, the majority of our diverse staff live overseas, with central support at the GWH office in Chapel Hill.
We are seeking a Machine Learning Scientist to support two projects:
- A large ultrasound dataset, including scans and associated biometry and clinical information, developed with the objective of creating algorithms for new portable ultrasounds, appropriate for low-resource settings.
- A 15,000 participant observational cohort dataset documenting the course and outcomes of labor, delivery and the immediate postpartum period in settings where adverse birth outcomes are high, with the objective of developing new tools to reduce intrapartum morbidity and mortality in low-resource settings.
- Proficient in typical ML programming and statistical packages, such as Python, Pandas, Pytorch, SQL, and SAS.
- Ability to develop software systems using modern software development and design tools such as Figma, C#, Flutter, etc.
- Extensive work experience in research implementation, ideally in low-resource settings
- Ability to identify and adopt cutting-edge ML techniques from the research literature
- Ability to formulate and implement novel machine learning end-to-end solutions to pertinent clinical problems (from idea to evaluation and interpretation of results).
- Demonstrated ability to work autonomously, to discern appropriate (and shifting) priorities and to organize workload efficiently.
- Excellent interpersonal communication and technical writing skills, including ability to produce research-grade publications.
including ability to produce research-grade publications.
- Familiarity with good clinical practices and protection of human subjects.
- Experience with clinical datasets.
- Experience with deep learning and popular development framworks such as Pytorch.
- Familiarity with cloud development environments (Azure ML).
- Background in computer vision and time series analysis.
- Familiarity with maternal-child health or international public health fields.
Not Applicable.
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