Position Summary

This position is necessary to:

Sustain day-to-day analytical operations of QUARC, including data processing, method development, and statistical support for funded projects.
Provide expertise in specialized quantitative and AI/ML methods (e.g., recurrence quantification analysis, fractal and complexity analyses, symbolic regression, deep learning, Bayesian modeling) that are central to the Core’s service portfolio.
Assist faculty and graduate student investigators with study design, data collection oversight, and manuscript preparation.
Support the development and maintenance of open-source software tools (e.g., R packages, MATLAB routines, Python libraries) associated with QUARC’s methodological contributions.
Contribute to grant preparation and reporting for Core-affiliated projects.
Develop and apply AI/ML pipelines for analysis of large-scale movement, physiological, and behavioral datasets.

Job Duties

Quantitative Data Analysis (30%)

Perform advanced nonlinear and statistical analyses of human movement, physiological, and behavioral time series data. Methods include but are not limited to: Recurrence Quantification Analysis (RQA), Detrended Fluctuation Analysis (DFA), Sample Entropy, Lyapunov Exponent estimation, Symbolic Regression, fractal analysis, and Bayesian multilevel modeling.

AI/ML Pipeline Development and Application (15%)

Design, implement, and evaluate machine learning and deep learning models applied to movement, sensor, and physiological data. Applications may include classification, regression, anomaly detection, foundation model fine-tuning, and AI-assisted discovery of governing equations of motion.

Methods Development and Software Support (15%)

Develop, test, and maintain computational tools and pipelines in R, MATLAB, and/or Python for Core users. Contribute to open-source packages and reproducible research workflows.

Research Collaboration and Consultation (20%)

Consult with faculty and student investigators on study design, data collection protocols, and appropriate analytical methods. Attend project meetings and contribute to team science efforts across QUARC-affiliated grants.

Manuscript and Grant Support (15%)

Assist with writing and editing manuscripts for peer-reviewed publication. Contribute analytical sections, figures, and methods descriptions to grant proposals and progress reports.

Required Qualifications

PhD in Biomechanics, Biomedical Engineering, Computational Neuroscience, Applied Mathematics, or a closely related field. ABD candidates will be considered; degree must be completed within six months of hire. Minimum 2 years of research experience (graduate or postgraduate) involving quantitative analysis of human movement, physiological, or sensor-based time series data. Demonstrated proficiency in R, MATLAB, and/or Python. Working knowledge of nonlinear dynamical analysis methods. Demonstrated experience applying machine learning or deep learning methods. Familiarity with statistical modeling approaches including multilevel/mixed effects models and Bayesian inference. Experience with at least one low-level or compiled programming language.