The 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.

Research Areas

Potential areas of methodological research include:

Innovative Clinical Trial Design

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.

Digital Twins and Counterfactual Prediction

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.

Survival, Longitudinal, and Complex Clinical Outcomes

Many pediatric oncology trials involve delayed, longitudinal, multistate, or otherwise complex outcomes that motivate new statistical methodology.

Your Role

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.

Requirements

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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