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Call for Applications: The AI Health Proposal Studios
The mission of Duke AI Health is to enable discovery, development, and implementation of artificial intelligence (AI) at Duke and beyond. A key component to achieving this goal is to foster high-impact, rigorous, and competitive proposals for scientific awards.
The AI Health Proposal Studios will provide a structured opportunity for investigators to engage with Duke’s top data science expertise and thought leadership, and to receive review and feedback of the scientific components of their proposals.
We are interested in research that is aligned with the mission of AI Health at the intersection of data science and clinical/translational science, especially predictive modeling, machine learning, and comparative effectiveness research (CER). Topics of interest include:
- Use of existing data and/or merging sources
- Developing/applying advanced quantitative methods with big data
- Research taking place within health systems, such as prospective use of predictive models
National Institutes of Health R21 (Exploratory/Developmental Research Grant) and R01 (Research Project Grant) proposals are examples of the types of applications that may be especially well-aligned with this initiative.
We would particularly like to support research teams who have results from small, internal data science projects (such as department-sponsored analyses or Duke Forge demonstration projects) that are ready to bridge into larger cohorts and populations.
Deadline: Dec. 7, 2020
Clinical and non-clinical investigators from any department at Duke are invited to apply.
Our goal is to foster health data science across Duke. We will welcome participation from non-School of Medicine investigators, with the goal of building more collaborations between faculty from the Schools of Medicine and Nursing and campus faculty.
We hope to see participation by a mix of investigators representing various levels of experience. We especially encourage investigators who are at the assistant professor/early associate professor level.