TTU Post Doctoral Research Associate

Develop and implement computational statistical methods for analyzing large‑scale, heterogeneous, and multi‑type datasets (e.g., continuous, categorical, functional, spatial, temporal, genomic, medical images).

Design Bayesian models and inference algorithms , including hierarchical models, latent variable frameworks, and Bayesian computation (MCMC, variational inference, sequential Monte Carlo).

Integrate multi‑modal data sources using advanced statistical fusion techniques, joint modeling, and representation learning to extract coherent signals across disparate data types.

Build and evaluate machine learning models —supervised, unsupervised, and semi‑supervised—tailored to scientific or engineering applications requiring statistical rigor and interpretability.

Develop scalable algorithms for high‑performance computing environments, including parallelization, GPU‑based computation, and optimization of statistical workflows.

Quantify uncertainty in predictive models using Bayesian posterior analysis, bootstrap methods, and sensitivity analysis to ensure robust scientific conclusions.

Collaborate with domain scientists to translate statistical ideas into actionable insights, ensuring methodological choices align with scientific objectives of HMAC.

Develop reproducible research pipelines using software tools (e.g., Python, R, Stan, Matlab, PyMC, TensorFlow) and maintain high standards of documentation and code quality.

The Human Molecular Aging Center (HMAC) is focused on advancing the understanding of molecular damage in brain aging. The center aims to create a comprehensive Human Molecular Atlas of Aging that spans multiple biological levels, including DNA, RNA, proteins, small molecules, organelles, and cells. The center will utilize cutting-edge analytical, biochemical, biophysical, and computational tools to explore molecular aging, with the goal of developing interventions to target the deleteriome and improve health span and longevity.