Research Assistant in the Division of Engineering Dr. Farah Shamout

Job description

The Clinical Artificial Intelligence Lab at NYU Abu Dhabi seeks to improve patient care by developing new machine learning methodologies that tackle unique computational problems in healthcare applications.

We use large real-world complex datasets, including data extracted from electronic health records and medical images, for applications pertaining to patient diagnostics and prognostics.

We are seeking a Research Assistant to join the team and make significant contributions to the field.

The researcher is expected to have (i) strong machine learning skills to improve model performance and robustness, and (ii) exemplary passion and motivation to pursue multidisciplinary research at the intersection of computing and healthcare.

Methodologies of interest include:


+ Multi-modal learning

+ Foundation models, including large language models

+ Agentic AI

+ Multi-agent AI systems

+ Transfer learning

+ Self-supervised learning

+ Federated learning


The Research Assistant will be primarily based at NYU Abu Dhabi.

The researcher will report directly to Dr. Farah Shamout and work in close collaboration with other researchers, PhD students, and undergraduate research assistants.

The researcher will engage with our regular collaborators across the NYU campuses and local medical institutions in the UAE.

Key Responsibilities


+ Research

+ Support the supervisor in developing and implementing the research agenda;

+ Conduct high-quality and innovative research primarily focused on ML for healthcare;

+ Design and implement experiments to compare proposed work with SOTA baselines;

+ Publish research findings in high-impact journals and conferences;

+ Communicate and present research findings at international academic gatherings;

+ Create, maintain, and document high-quality research code for reproducibility;

+ Maintain good practice in managing and accessing sensitive medical datasets;

+ And collaborate with scientists within the NYU Global Network and in Abu Dhabi.


Minimum Qualifications


+ Currently has or is in the process of completing a bachelor’s or master’s degree from a recognized institution

+ Bachelor’s/ Master’s degree in computer science, mathematics, computer engineering, or relevant technical field

+ Demonstrable research experience involving data pre-processing and preparation for machine learning models

+ Demonstrable research experience in conducting experiments for training and evaluating deep neural networks

+ Knowledge of multi-modal learning, transfer learning, transformers, or self-supervised learning

+ Experience in dealing with large medical datasets (e.g., electronic health records data or medical images)

+ Ability to use high performance computing cluster

+ Proficient programming experience in Python and libraries (e.g., Pytorch, TensorFlow)

+ Experience in maintaining high-quality code on Github

+ Experience in running and managing experiments using GPUs

+ Ability to visualize experimental results and learning curves

+ Effective inter-personal and team-building skills

+ Self-motivated with an ability to work independently and in a team to get the work done

+ Excellent communication skills (oral and written communication)

+ Willingness to learn and confront new challenges


Preferred Qualifications


+ Bachelor’s/ Master’s thesis conducted in the area of machine learning for healthcare and related topics

+ First-author peer-reviewed published papers (or under review)

+ Evidence of leadership and service activities in the academic domain


For consideration, applicants need to submit a cover letter, curriculum vitae with full publication list, research statement (1-page), project proposal summary (1-page), and three letters of reference, and a transcript, all in PDF format.

If you have any questions, please email Prof.

Farah Shamout at farah.shamout@nyu.edu.

The terms of employment are very competitive and include housing and educational subsidies for children.

Applications will be accepted immediately and candidates will be considered until the position is filled.

Please visit our website at http://nyuad.nyu.edu/en/about/careers/faculty-positions.html for instructions and information on how to apply.

About NYUAD:

NYU Abu Dhabi is a degree-granting research university with a fully integrated liberal arts and science undergraduate program in the Arts, Sciences, Social Sciences, Humanities, and Engineering.

NYU Abu Dhabi, NYU New York, and NYU Shanghai, form the backbone of NYU’s global network university, an interconnected network of portal campuses and academic centers across six continents that enable seamless international mobility of students and faculty in their pursuit of academic and scholarly activity.

This global university represents a transformative shift in higher education, one in which the intellectual and creative endeavors of academia are shaped and examined through an international and multicultural perspective.

As a major intellectual hub 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/Females/Vet/Disabled/SexualOrientation/Gender Identity Employer

UAE Nationals are encouraged to apply.

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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.

All interested persons are encouraged to apply for vacant positions at all levels.

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Required Skill Profession

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