The Harvard Program in Therapeutic Science (HiTS) and the Food and Drug Administration (FDA) invite applications for a fellowship program in Artificial Intelligence and Machine Learning with FDA’s Information Exchange and Data Transformation (INFORMED) Program. The Fellow will pursue the design, validation, and implementation of AI/ML-based solutions and other computational methods and modeling techniques that can have a positive impact on regulatory review, drug development, patient outcomes, and related workflows. The Fellow will work at both Harvard Medical School in Boston, Ma and the FDA White Oak campus in Maryland. Travel and lodging will be supported by the Fellowship.
An incubator by design, INFORMED is focused on driving solutions in agile technology and data science in support of FDA’s mission of advancing public health. INFORMED serves as a catalyst for the development of novel approaches to improve regulatory science through collaborative research in an entrepreneurial environment. As a multi-disciplinary effort, INFORMED is building technical and organizational capabilities for big data analytics, working with diverse data sets from sources such as clinical trials, electronic health records and biosensor technologies.
The Fellow will work with faculty across Harvard and FDA to conduct high impact regulatory science research. This position has no teaching or administrative duties. This is a term appointment, not to exceed 2 years.
Research activities include, but are not limited to:
Designing, developing and implementing machine learning and artificial intelligence algorithms for regulatory science applications
Developing new clinical endpoints and signal detection methods for evaluation of the safety and effectiveness of therapies
Developing new approaches for understanding variations in individual patient experience using diverse data sets from clinical trials, electronic health records, and biometric monitoring devices
Supporting the development of principles and definitions for validity and strength of AI/ML-derived evidence in the context of product approval and regulations
PhD in computer science, engineering or related field
Experience in Artificial Intelligence/Deep Learning/Machine Learning
Strong background in numerical optimization and statistics, including Bayesian modeling/inference
Advanced proficiency in at least one programming language (e.g., Python, R)
Strong team player with excellent communication skills and interest in learning about regulatory science
Experience in healthcare, biomedical research, or drug development is preferred but not required
Application Procedure and Requirements:
Applications will be accepted until the position is filled. Please include the following when applying:
Curriculum vitae and a copy of academic records as a single PDF (unofficial records are acceptable)
Cover letter and a 2-page description of relevant experience as a single PDF
Contact details of at least two references
DOI or PMCID of up to three relevant publications
Host Institutions: Harvard Medical School and the Food and Drug Administration
Location: Boston, MA and White Oak, MD; travel required