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

[Krzysztof Geras]

 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 at nyu.edu.
The title of the email should start with the string “[postdoc, deep
learning for breast cancer]”.

Best,
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.
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