St. Jude Children's Research Hospital, Inc.
Hiring now
Postdoctoral Research Associate - Statistical Methods for Pediatric Oncology Clinical Trials
United StatesFull-timePosted 2 months ago
St. Jude Children's Research Hospital, Inc.
ApplySt. Jude Children's Research Hospital, Inc.
Hiring now
United StatesFull-timePosted 2 months ago
St. Jude Children's Research Hospital, Inc.
ApplyThe research program focuses on methodological challenges arising in pediatric and rare-disease settings, where patient populations are often small, outcomes may be delayed or complex, and conventional randomized trial approaches may not always be feasible. The successful candidate will work at the intersection of innovative clinical trial design, causal inference, external controls and real-world evidence, digital twins and counterfactual prediction, and statistical methods for survival, longitudinal, and other complex outcomes.
Clinical applications will focus primarily on pediatric solid tumors, including neuroblastoma and sarcoma, as well as emerging cellular and immunotherapy studies such as CAR-T therapy.
The position provides substantial flexibility for the postdoctoral researcher to develop an independent methodological research program based on the candidate’s background and interests, emerging scientific opportunities, and important problems arising from ongoing pediatric oncology research.
Potential areas of methodological research include:
Development of efficient and rigorous statistical methods for early- and mid-phase pediatric oncology trials, particularly in settings involving small populations, rare diseases, heterogeneous treatment response, or delayed outcomes.
Causal Inference, External Controls, and Real-World Evidence
Development of principled approaches for incorporating external information into clinical trials when concurrent randomized control groups are limited or infeasible.
An emerging research direction is the development and evaluation of digital twins and counterfactual prediction methods for clinical trials.
Rather than viewing a digital twin solely as a prediction model, we are interested in understanding when model-based predictions can provide clinically and statistically credible information about outcomes under alternative treatment strategies.
Many pediatric oncology trials involve delayed, longitudinal, multistate, or otherwise complex outcomes that motivate new statistical methodology.
The postdoctoral researcher will have opportunities to:
Develop new statistical methodology motivated by important pediatric oncology problems
Conduct simulation studies to evaluate statistical operating characteristics
Analyze clinical trial, registry, and real-world datasets
Develop statistical software in R and/or Python
Collaborate closely with pediatric oncologists, clinical investigators, statisticians, and data scientists
Participate in the design and analysis of innovative pediatric oncology clinical trials
Publish methodological and applied research in leading statistical, clinical trial, and medical journals
Present research at national and international scientific meetings
Develop independent research ideas and a coherent methodological research program
Contribute to collaborative grant proposals and future independent funding applications
Participate in mentoring and research activities within the Department of Biostatistics
The balance between methodological development and applied collaboration can be tailored to the candidate’s background, interests, and career goals.
We are looking for a candidate with strong quantitative training who is interested in developing statistical methodology motivated by challenging clinical problems.
Ideal candidates will have:
A PhD in biostatistics, statistics, epidemiology, data science, or a closely related quantitative discipline
Strong training in statistical methodology
Experience with statistical programming, preferably in R and/or Python
Strong written and oral communication skills
Ability to work effectively in multidisciplinary research teams
Interest in clinical trials and biomedical research
Experience in one or more of the following areas would be particularly valuable:
Clinical trial design
Survival analysis
Bayesian statistics
Causal inference
External controls or real-world evidence
Target trial emulation
Longitudinal data analysis
Machine learning or causal prediction
Pediatric oncology
Prior experience in pediatric oncology is not required. Candidates with strong methodological training who are interested in developing expertise in pediatric cancer research are encouraged to apply.
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