Post-doctoral Fellowship in Artificial Intelligence and Machine Learning at Harvard Medical School and the Food and Drug Administration (Boston, MA and White Oak, MD

Description

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

Basic Qualifications

  • 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

How to Apply

Apply at https://hitsedu.bamboohr.com/jobs/view.php?id=6.