TTU Post Doctoral Research Associate

Texas Tech University • Lubbock, TX

5d

About The Position

Performs specialized Post Doctoral work in the planning, conducting and/or supervision of original research. Responsible for participating in a research project associated with PhD studies and the interpretation of the results of publication. Work is performed under supervision of graduate faculty members with evaluation based on accomplishment of assigned objectives and overall effectiveness of project. May supervise research and student assistants.

Requirements

• PhD in area of project specialization.
• Knowledge of modern research practices, the methods, resources, and standards thereof.
• Ability to organize work effectively, conceptualize and prioritize objectives and exercise independent judgment based on an understanding of organizational policies and activities.
• Ability to integrate resources, policies, and information for the determination of procedures, solutions and other outcomes.
• Ability to establish and maintain effective work relationships with other employees and the public.
• Ability to plan and allocate the workload of employees, providing direct training and supervision as needed.

Responsibilities

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree