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New York University Postdoctoral Associate Positions in Representation Learning in Healthcare in New York, New York

The Courant Institute of Mathematical Sciences at New York University (NYU) and the Department of Radiology at the NYU Grossman School of Medicine invite applications for two Postdoctoral Research Associates to work on projects in the interdisciplinary field of Machine Learning and Healthcare. The project’s goal is to learn unified high-dimensional distributed representations of patients using healthcare data from multiple modalities, including medical imaging, electronic health records (EHRs), and wearable sensors, among others. Furthermore, the projects will also involve exploring ways to leverage longitudinal (temporal) patient data to learn such representations.

The postdocs will work with Prof. Sumit Chopra ( and Prof. Daniel Sodickson ( . Learning accurate and useful patient representations will require working at the intersection of machine learning, signal processing, and physics-based modeling. There are many scenarios under which such representations can be learned, including using unimodal/multimodal and static/dynamic (temporal) datasets. Despite the enormous promise of machine learning applications in healthcare, the deployment of machine learning models in real clinical settings has remained elusive. The projects will also focus on exploring ways to utilize representations to derive real clinical benefits, such as improving diagnostic accuracy, speeding up image acquisition, and proposing personalized treatment plans.

Through this position, you will be a part of (and working at the intersection of) two of the most sought-after schools in their respective fields, namely the Courant Institute (a leader in applied mathematics and machine learning) and the Grossman School of Medicine (a leader in healthcare research and delivery). This opportunity will also allow you to readily deploy your research in a clinical setting within the hospital, thereby enabling you to make a real-world impact, saving and/or improving the lives of real people.

The positions are available immediately. They are full-time appointments for one year, with the possibility of renewal for up to three years. The renewal is subject to satisfactory performance and availability of funds.

  • Ph.D. or be close to completing a Ph.D. in Computer Science, Mathematics, Statistics, Physics, or a related discipline

  • Possess strong research experience and a solid publication record in Machine Learning

  • Expert level programming experience

  • Should have a strong interest in applying Machine Learning models to Healthcare problems

  • Be comfortable working independently and as part of an interdisciplinary team

  • Experience in Healthcare is highly desirable though it is not necessary

Applicants should submit the following set of items:

  • Curriculum Vitae with a list of publications

  • Three representative publications co-authored by you

  • Research Statement describing your research experience, your research interests, and how your interests fit with the goals of the project

  • Letters of recommendation from at least 3 referees

For further information or questions please reach out to Prof. Sumit Chopra (

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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, reproductive health decision making, 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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