Postdoctoral researcher position in deep learning for medical image analysis at NYU

We are looking for a postdoctoral researcher to join our efforts in developing deep learning methods for early diagnosis of breast cancer during screening mammography. We intend to work both on methods improving the accuracy as well as explaining the prediction in a human-interpretable form.

The candidate will be based primarily at the NYU School of Medicine (Prof. Krzysztof J. Geras, Prof. Linda Moy and Prof. S. Gene Kim) and will work in close collaboration with the NYU Center for Data Science (Prof. Kyunghyun Cho).

Expected qualifications:
- PhD (or near completion) in machine learning or related discipline,
- experience with deep neural networks for computer vision,
- excellent programming skills,
- ability to work in an interdisciplinary team with members at various levels.

Some examples of our recent work:
- arxiv.org/pdf/1703.07047.pdf
- arxiv.org/pdf/1711.03674.pdf
- arxiv.org/pdf/1805.08249.pdf

Start date: As soon as possible.
Please apply by no later than the 10th of August.

The initial appointment will be for a year, with an option to renew further, depending on performance and funding.

To be considered for this position, please send your CV with a list of publications and the contact details of two references to: k.j.geras@nyu.edu. The title of the email should start with the string “[postdoc, deep learning for breast cancer]”.

Krzysztof J. Geras,
Kyunghyun Cho,
Linda Moy,
S. Gene Kim

NYU is an Equal Opportunity Employer and is committed to a policy of equal treatment and opportunity in every aspect of its recruitment and hiring process without regard to age, alienage, caregiver status, childbirth, citizenship status, color, creed, disability, domestic violence victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, sex, sexual orientation, unemployment status, veteran status, or any other legally protected basis. Women, racial and ethnic minorities, persons of minority sexual orientation or gender identity, individuals with disabilities, and veterans are encouraged to apply for vacant positions at all levels.