The biostatistician postdoctoral fellow will provide advanced statistical expertise to support imaging-based research across clinical and translational domains within the QUANTX Center. This role focuses on leading study design, performing quantitative analyses of complex, multimodal datasets, and developing reproducible analytical pipelines and analysis-ready data structures to enable high-impact scientific discovery. The fellow will collaborate with multidisciplinary teams of radiologists, neuroscientists, data scientists, and biostatisticians to support research spanning neuroimaging, breast, prostate, and whole-body imaging, as well as AI-driven and quantitative imaging analytics. This position offers a highly collaborative, data-intensive environment with opportunities to contribute to grant development, publications, and the advancement of innovative methodologies in precision medicine.

Minimum Qualifications:

• PhD in Statistics, Biostatistics, Bioinformatics, or a closely related quantitative field

Preferred Qualifications:

• Experience working with neuroimaging or other medical imaging modalities (e.

g., MRI, f

MRI, DTI, CT

)

• Experience integrating multimodal datasets (e.g., imaging, behavioral, clinical, physiological data
• Familiarity with statistical validation techniques for predictive modeling (e.g., ROC, AUC, cross-validation)
• Experience support grant submissions (e.g., NIH, DoW) and contributing to peer-reviewed publications
• Exposure to AI-driven or quantitative imaging pipelines
• Prior experience working in multidisciplinary research teams or shared research core environments
• Demonstrated expertise in statistical programming using R, SAS, SPSS, and/or Python
• Strong foundation in advanced statistical methodologies, including modeling of complex and high-dimensional data
• Familiarity with analysis of biomedical and/or imaging datasets
• Working knowledge of ML methods and their application in research settings
• Ability to develop reproducible workflows and maintain well-documented code

Special Skills/Trainings:

• Strong analytical problem-solving skills with attention to detail and data integrity
• Ability to communicate complex statistical concepts to clinical dn non-technical audiences
• Strong organizational and project management skills in a collaborative research setting
• Commitment to reproducible research practices and data governance standards

Additional Application Materials Required:

• Curriculum Vitae (CV)
• Cover letter outlining research interests, technical expertise, and relevant experience
• (Optional) Representative publications or code samples Special Instructions to the

Applicants:

• Applications will be reviewed on a rolling basis until filled
• Candidates should highlight experience with statistical modeling, reproducible workflows, and any imaging or AI-related research
• Interest in translational research and multidisciplinary collaboration is strongly encouraged

Responsibilities:

Study Design and Experimental Planning Collaborate with investigators to design statistically rigorous imaging studies, including cross-sectional, longitudinal, and interventional designs Perform power calculations and sample size estimation for imaging-based endpoints Define primary and secondary study endpoints aligned with research objectives Develop comprehensive statistical analysis plans (SAPs) for grant submissions and IRB protocols Quantitative Imaging Data Analysis Apply advanced statistical methodologies, including linear and nonlinear mixed-effects models, to imaging datasets Conduct multivariate and high-dimensional data analyses appropriate for complex biomedical data Perform statistical analyses of time-series data, including functional imaging and longitudinal measures Translate complex quantitative results in actionable insights for clinical and translational research Integration with AI and Quantitative Imaging Pipelines Collaborate with data scientists and imaging specialists to integrate statistical rigor into ML and AI-driven workflows Develop and implement statistical frameworks for model validation, including ROC, AUC, calibration, and decision curve analyses Ensure robustness and generalizability of predictive models by addressing overfitting, bias, and validation strategies Contribute to multimodal data integration efforts across imaging, behavioral, and clinical datasets Reproducible Research and Data Management Develop and maintain reproducible analytical pipelines in R, SAS, SPSS, and/or Python Ensure adherence to FAIR (Findable, Accessible, Interoperable, Reusable) data principles Maintain version-controlled code repositories and comprehensive documentation to support transparency and reproducibility Support development of analysis-ready datasets and standardized data structures for downstream analyses Collaborative Research and Scientific Contribution Provide statistical consultation to Core users and affiliated investigators across multidisciplinary teams Participate in imaging core meetings and collaborative research initiatives Contribute to manuscript preparation, conference abstracts, and peer-reviewed publications Support development of competitive grant applications (federal and nonprofit) Mentor trainees and junior investigators in statistical methods and reproducible research practices Assist in development of workshops and education activities within the Center