NYU Abu Dhabi’s Clinical Artificial Intelligence Lab seeks to improve patient care by developing new machine learning (ML) methodologies that tackle unique computational problems in healthcare applications. We use large, complex real-world datasets, including data extracted from electronic health records and medical images, for patient diagnostic and prognostic applications.
We are looking for a postdoctoral associate to join the team and make a significant contribution to the field. The researcher should have (i) strong machine learning skills to improve the accuracy, robustness, and explainability of models, and (ii) exemplary passion and motivation to pursue multidisciplinary research at the intersection of computer science and health care. Methodologies of interest include:
- multimodal learning,
- learning transfer,
- graphical neural networks,
- and self-supervised learning.
The Postdoctoral Associate will be primarily based at NYU Abu Dhabi. The researcher will report directly to Dr. Farah Shamout and work closely with other researchers, PhD students and undergraduate research assistants. The researcher will collaborate with our regular collaborators from the NYU Center for Data Science, NYU Radiology and NYU Tandon School of Engineering, as well as local medical institutions in the United Arab Emirates.
- Support the supervisor in the development and implementation of the research program;
- Conduct innovative and high quality research primarily focused on the development of ML methodologies for healthcare;
- Generate new high-impact ideas based on gaps and limitations of the state of the art (SOTA);
- Design and implement experiments to compare proposed work with SOTA baselines;
- Publish research results in high-impact journals and conferences;
- Communicate and present research results at international academic gatherings;
- Create, maintain and document high quality research code for reproducibility;
- Maintain good management and access practices for sensitive medical data sets;
- Assist the supervisor in the preparation of grant applications (if applicable);
- And collaborate with scientists within the NYU Global Network and in Abu Dhabi.
Training & professional development*
- Attend training and workshops for career development;
- Mentor of PhD students and undergraduate research assistants (if applicable);
- Actively participate in NYU Abu Dhabi events and committees, such as the Postgraduate Council Steering Committee;
- Gain experience applying for local research grants (subject to eligibility);
- And transition to independence to pursue a career of choice following appointment.
* The researcher will create a personalized training and development plan with the supervisor.
- Currently has or is in the process of completing a Ph.D., MD/PhD, DPhil or equivalent terminal degree from a recognized institution (no more than 5 years since obtaining the Ph.D.)
- Doctoral research in the field of machine learning and artificial intelligence
- Bachelor’s/Master’s degree in Computer Science, Mathematics, Computer Engineering or a relevant technical field
- Articles published by the first author and peer-reviewed (or under review)
- 3+ years of demonstrable research experience involving data preprocessing and preparation of machine learning models
- 3+ years of demonstrable research experience conducting experiments for training and evaluating deep neural networks
- Extensive programming experience in Python and libraries (e.g. Pytorch, TensorFlow) with several years of practice
- Experience in maintaining high quality code on Github
- Experience running and managing experiments using GPUs
- Ability to visualize experimental results and learning curves
- Effective interpersonal skills and team spirit
- Self-motivated with an ability to work independently and as part of a team to get the job done
- Excellent communication skills (oral and written communication)
- Willingness to learn and take on new challenges
- Doctoral research conducted in the field of machine learning for healthcare and related topics
- In-depth knowledge of multimodal learning, transfer learning, graphical neural networks, transformers or self-supervised learning
- Experience in handling large sets of medical data (e.g. electronic health record data or medical images)
- Ability to use a high performance computing cluster
The appointment will be for two years, with the possibility of renewal based on performance (in total not exceeding 5 years). Terms of employment are competitive and will include subsidies for housing and travel. Applications will be accepted immediately and candidates will be considered until the position is filled. To be considered, all applicants must submit a cover letter, up-to-date CV, publication list, research statement (1 page), project proposal summary (1 page), and at least three letters of recommendation, all in PDF Format. If you have any questions that the job posting does not answer, please email: email@example.com
Email applications will NOT be considered.
NYU Abu Dhabi is a degree-granting research university with a fully integrated liberal arts and science undergraduate curriculum in the arts, sciences, social sciences, humanities, and engineering. NYU Abu Dhabi, NYU New York, and NYU Shanghai form the university backbone of NYU’s Global Network, an interconnected network of portal campuses and academic centers on six continents that enable seamless international mobility of students and faculty across their pursuit of academic and scholarly pursuits. . This global university represents a transformative change in higher education, in which the intellectual and creative endeavors of academia are shaped and examined from an international and multicultural perspective. As a major intellectual center at the crossroads of the Arab world, NYUAD serves as a center for scholarly thought, advanced research, knowledge creation and sharing, through its academic, research and creative activities.
EOE/AA/Minorities/Women/Vet/Disabled/Sexual Orientation/Gender Identity Employer
Nationals of the United Arab Emirates are encouraged to apply.
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