Data-Driven Compact Thermal Propulsion Systems for High-Energy-Density Fuels - College of Engineering - Purdue University

Description

This project will advance the scientific foundations for compact, ultra–energy efficient propulsion systems powered by high-energy-density domestic fuels, including bio-derived and synthetic fuels, liquid hydrogen and natural gas. The research directly supports the vision of a planned Testing of Universal Large-scale Innovative Propulsion Systems (TULIP) Research Center, which aims to reduce U.S. reliance on foreign-sourced fuels and accelerate innovation in high-performance thermal engines.

The postdoctoral fellow will develop advanced experimental and data-driven approaches to characterize combustion kinetics & dynamics, heat transfer, and turbine performance under realistic flow conditions. Using novel diagnostics, real-time data assimilation, and machine learning tools, the project will establish predictive frameworks to optimize engine cycles and rapidly validate new designs. This will enable unprecedented speed in evaluating new engine architectures and fuel-flexible propulsion systems.

The research will contribute to national security and economic competitiveness by creating propulsion technologies that leverage domestically produced fuels, while simultaneously training the next generation of propulsion engineers in cutting-edge methods. By uniting experimentation, computation, and data science, with advances in materials, AI, and energy policy, this project lays the foundation for TULIP to become a national hub for sustainable aviation propulsion. 

Start Date

August 24, 2026

Postdoc Qualifications

The ideal candidate will hold a Ph.D. in Aerospace Engineering, Mechanical Engineering, or a closely related discipline. The position requires a strong background in experimental methods with demonstrated experience in testing, advanced measurement techniques, and data processing relevant to high-speed flow or propulsion environments. While prior specialization in combustion or turbines is not mandatory, familiarity with energy systems and experimental facility operations will be advantageous.

Equally important are strong data analysis and interpretation skills, as the project will involve integrating experimental results with data-driven modeling and real-time assimilation approaches. Experience with modern tools for statistical analysis, uncertainty quantification, or machine learning is desirable.

The candidate must also demonstrate excellent teamwork and communication skills. This role will require effective integration into the collaborative research teams led by Prof. Paniagua and Prof. Qiao at Purdue University, working across experimental, computational, and policy-oriented thrusts. The successful applicant will be an outstanding team player, capable of contributing to a diverse, interdisciplinary research environment while also driving independent research directions.

Overall, the position is best suited for a highly motivated researcher eager to contribute to next-generation sustainable propulsion systems through a combination of experimentation, data science, and collaboration. 

Co-advisors

Prof. Guillermo Paniagua, gpaniagua@purdue.edu, School of Mechanical Engineering. https://engineering.purdue.edu/PETAL/
Prof. Li Qiao, lqiao@purdue.edu, School of Aeronautics and Astronautics. https://qiaoresearchgroup.com

Bibliography