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Internships In Machine Learning Research

Company

Center for Advanced Imaging Innovation and Research (CAI2R)

Address , New York, 10016
Employment type INTERN
Salary $15.00 - $44.99 an hour
Expires 2023-10-10
Posted at 9 months ago
Job Description

The Center for Advanced Imaging Innovation and Research (CAI2R) at NYU Langone Health is offering part-time internships to graduate students interested in machine learning research on medical imaging and clinical language data.

Machine learning interns will work with an interdisciplinary group comprising research scientists, research faculty, postdoctoral fellows, clinicians, and engineers. The internship offers a unique opportunity not only to develop ML models and work on complex, novel problems, but also to get involved with interdisciplinary research in the exciting field of biomedical imaging.

Key Responsibilities

  • Research and Development: Contribute to projects that target challenging problems at the intersection of ML and medical imaging. Potential projects include deep learning-based image reconstruction for MRI, development of natural language processing model applications to clinical datasets, or the design and testing of a deep learning and artificial intelligence tool to infer presence of disease.
  • Presentation: Present the results of your work at the end of summer to the CAI2R community. Summer projects may also contribute to ongoing research leading to publication.
  • Communication: Regularly meet and communicate the status of the project, results, and finding with senior supervisors. Interact with senior research engineers to develop an understanding of the project needs and requirements.

Minimum Qualifications

  • Interest in and passion for working on complex problems to improve healthcare.
  • Thorough understanding of ML fundamentals, probability, and statistics.
  • Ability to collaborate and communicate effectively as a member of a research engineering team.
  • Familiarity with software development tools, e.g., Jupyter notebook, Git version control.
  • Strong programming skills in Python and experience with common machine learning and computer vision libraries, e.g., PyTorch, TensorFlow, OpenCV.
  • At least 1 year completed of a master’s or PhD program in computer science, data science, or closely related area, including completed coursework in deep learning and machine learning.

Preferred Qualifications

  • Prior experience with medical imaging and data; familiarity with MRI Python packages pydicom, nibabel.
  • Research experience in ML, computer vision, natural language processing, signal processing or data mining.

About Us

CAI2R (pronounced care) comprises approximately 150 full-time personnel dedicated to imaging research, development, and clinical translation. Our team is diverse and highly collaborative. We work in interdisciplinary, matrixed groups that include engineers, scientists, clinicians, technologists, and industry experts.

Joining our center means becoming part of a diverse community that values crosspollination of ideas, celebrates creativity, and nurtures an environment conducive to breakthrough innovations.

Learn more about our mission, our research, and out team.

Environment

The Center for Advanced Imaging Innovation and Research is supported by the NIH and operated by the department of radiology at NYU Langone Health.

Learn more about our research facilities.

Internship Location and Pay

The internships are part-time and are located onsite at our facility in New York City for their full duration.

The salary range is $15-44.99 per hour.

We are committed to diversity and inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration without regard to race, color, gender, gender identity or expression, sexual orientation, national origin, age, religion, creed, or disability.

To Apply

To be considered, send your resume or CV to [email protected] with the subject line “CAI2R Internship Application—Your Name.” Links to published articles or projects on GitHub are appreciated.