The Post-Doctoral Research Associate is responsible for conducting innovative research and develop novel algorithms, models, and techniques for application of systems modeling and AI to Healthcare and other complex systems. This includes the collaboration with the team members, and interact with the other collaborators. This applies to one or more related or unrelated assigned areas of responsibility. Position will supervise the work of others as it relates to data collection, modeling development, and project management.

Required Qualifications

Education: PhD in Industrial Engineering, Computer Science, or Systems Engineering, and/or related field.

Experience:

• AI & Machine Learning - Hands-on experience with LLMs, generative AI, RAG architectures, fine-tuning, prompt engineering, embeddings, and vector databases (e.g., FAISS). Solid grounding in supervised learning, probabilistic modeling, and model evaluation.
• Optimization & Computational Methods - Working knowledge of gradient-based and heuristic optimization, genetic/evolutionary algorithms, constrained and multi-objective optimization, and parameter calibration. A strong operations research foundation is expected.
• Advanced Python proficiency. We expect modular, quality research code, API development, version control (Git/GitHub), and the ability to handle both structured and unstructured data at scale.
• Simulation & Systems Modeling - Proficiency in some or all of the following modeling techniques: system dynamics (stock-flow structures, calibration, sensitivity analysis), agent-based modeling with scalable architectures, and stochastic/Monte Carlo simulation. We appreciate interest in hybrid modeling combing various modeling types to optimize the applicability and utility of the modeling solution. Strong model validation skills against empirical data are essential.

Knowledge, Skills, Abilities:

• Strong ability to work independently
• Effective management, and organizational skills
• Decision making, planning, risk management sponsor management, project management, quality management, research skills
• Basic understanding of and experience in proposal development
• Necessary analytical skills to manage price and negotiate research proposals
• Excellent oral and written communication skills
• The ability to manage multiple research projects simultaneously, and an ability to establish positive relationships with a wide variety of constituents and diverse groups. Specifically:
  • Knowledge of systems modeling, simulation (SD, ABM), and stochastic processes
  • Knowledge of machine learning, LLMs, and AI-enabled modeling approaches
  • Skill in Python programming, data engineering, and scalable software development
  • Skill in optimization methods and computational analysis
  • Ability to design, validate, and interpret complex simulation models
  • Ability to manage multiple research projects and work independently
  • Ability to communicate technical concepts effectively across disciplines
  • Ability to collaborate with diverse stakeholders and research teams

Applicants must be legally authorized to work in the United States on a full-time basis without need now or in the future for sponsorship for employment-based visa status.

Preferred Qualifications

Experience:

• Prior applied research experience in one or more of the following is highly desirable: healthcare systems modeling, disaster response, infrastructure resilience, energy systems analysis, maintenance/asset management, or policy simulation.
• Advanced Technical Skills
• GPU deployment experience, Docker/containerization, HPC environments, knowledge graph integration, and familiarity with RAG-based architectures beyond prototyping.
• Research Track Record
• A peer-reviewed publication record is strongly preferred. Prior involvement in grant proposal development and technical presentations to external audiences adds significant value.
• Mentorship & Leadership
• Experience mentoring graduate students and leading sub-projects independently. Demonstrated ability to collaborate across disciplines and communicate complex technical concepts to non-specialist audiences.

Work Location

• Location: Knoxville, TN
• Onsite

Compensation and Benefits

• Anticipated hiring range: $75,100 - $81,700
• Find more information on UT Benefits here

Application Instructions

To express interest, please submit an application with the noted below attachments. To be assured of full consideration, completed applications with all requested materials should be submitted on or before May 1, 2026:

• Resume/CV
• Cover Letter

About the Applied Systems Laboratory

The Applied Systems Lab (ASL) at the University of Tennessee, Knoxville is a dynamic, multidisciplinary research environment where cutting-edge computational science meets the most pressing challenges of our time. Our researchers bring together systems engineering, artificial intelligence, simulation modeling, and optimization to understand, predict, and improve the behavior of complex real-world systems. From cascading failures in critical infrastructure to the intricate dynamics of healthcare delivery, from disaster response networks to energy systems under stress, we build the models, develop the algorithms, and generate the insights that decision-makers need to act with confidence.

What makes ASL distinctive is our commitment to integration and impact. We forge hybrid frameworks that combine the explanatory power of system dynamics and agent-based modeling with the pattern recognition of machine learning and the rigor of advanced optimization. Our team is intentionally multidisciplinary, with engineers, data scientists, and domain experts collaborating across boundaries because the hardest problems don't respect them. Our work informs policy, supports emergency preparedness, strengthens infrastructure resilience, and pushes the frontier of what is possible when rigorous science is applied to systems that matter.

• Modeling & Simulation Development: Design and implement system dynamics, agent-based, and stochastic simulation models; conduct calibration, validation, and sensitivity analysis; develop hybrid AI-simulation frameworks.
• AI / Data Systems Development: Develop machine learning and LLM-enabled systems (RAG, embeddings, vector databases); build data pipelines (ETL), scalable architectures (e.g., Spark), and model evaluation pipelines.
• Optimization & Computational Methods: Apply optimization techniques (gradient-based, heuristic, multi-objective); perform parameter estimation and scenario optimization for decision support.
• Research & Scholarly Activity: Design computational experiments; publish research; contribute to grant proposals and technical presentations.