The Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory is seeking to appoint one or more highly motivated postdoctoral researchers with expertise in artificial intelligence/machine learning and data science. Applicants must have completed their Ph.D. studies within the past two years. Prior to submitting an application, they must identify an existing RAD Collaboratory faculty member as their postdoctoral mentor and provide the Mentor’s Letter of Nomination (on Rutgers Letterhead) with their application materials. Successful candidates will work under the supervision of the Nominating Mentor and contribute to interdisciplinary research projects focused on important problems in science and engineering. Eligible subject areas include computational biology and/or biomedicine, multi-modality fusion and inference (visual and NLP data), computationally efficient ML, environmental sciences, and business applications. The RAD Collaboratory offers a rich environment for collaboration with ample access to computational resources.
Research Programmer/Data Scientist - Research Associate
Summary:
The Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory is seeking one or more Research Scientists to support leveraging modern Machine Learning (ML) and Deep Learning (DL) techniques by Rutgers faculty, postdoctoral fellows, and students. The ideal candidate will have a strong ML/DL and cyberinfrastructure (CI) background and a willingness to contribute to interdisciplinary research across diverse basic and applied science and engineering domains.
Responsibilities will include:
● Design, develop, and deploy ML/DL algorithms for domain science and engineering fields
● Support RAD Collaboratory research on national cyberinfrastructure (e.g., ACCESS, NAIRR, and DOE supercomputers) or cloud environments (e.g., AWS, GCP, and Azure)
● Support application of ML/DL/CI techniques across topics and domains
● Deliver training on ML/DL/CI techniques and best practices to a broad range of researchers
● Stay at the forefront of new ML/DL techniques and ML/DL systems that support science and engineering research
● Co-author peer-reviewed interdisciplinary research publications
● Contribute to funding applications from external sources (e.g., NSF, NIH)
Please apply at: