Assistant Professor, Associate Professor, or Professor in Non-Combustion Reaction, and Data Analytics​/AI; RISES

The Opportunity

The University of Central Florida (UCF) has established several interdisciplinary faculty research clusters to strengthen its academic offerings and research mission. In support of this effort, we invite candidates for two (2) tenure-earning assistant professor, tenured associate professor, or tenured professor positions with the university research center on Resilient, Intelligent, and Sustainable Energy Systems (RISES). The positions have an anticipated start date of August 8, 2026.

These are interdisciplinary positions that will be expected to strengthen both the RISES Center and a chosen tenure home department and may include a combination of joint appointments. A strong advantage of this position is the ability of the candidate to choose multiple units for their joint appointment in the College of Engineering and Computer Science, the College of Sciences, or both.

All candidates whose research are at the forefronts of energy systems, resilience, and sustainability will be considered. The technical fields of specific research interest for the two positions include but are not limited to:

Position # 1
- Sustainable Energy Systems:
Non-Combustion (
Host Department: Mechanical and Aerospace Engineering
):

An ideal candidate will have extensive expertise in pathways for novel processes of electric power generation and storage that do not involve combustion. The selected candidate should be proficient in process design, characterization, and modeling of low carbon electricity generation technologies that ensure access to affordable, reliable, and sustainable power for all. We seek candidates with a strong foundation in fluid mechanics, thermodynamics, heat and mass transfer, and energy systems engineering, and a demonstrated research record in non-combustion-based electric power generation and/or energy storage.

Areas of particular interest include, but are not limited to:

• Advanced nuclear energy systems
  , including small modular reactors (SMRs), microreactors, molten salt reactors, liquid metal reactors and high-temperature gas cooled reactors, especially those supporting grid resilience.

• Hydrogen production and storage
  , particularly hydrogen derived from nuclear or natural gas with carbon capture (blue hydrogen).

• Thermal energy storage and high-efficiency heat exchangers for industrial and grid-scale applications.

• Electrochemical systems
  , including next-generation batteries and fuel cells for defense, aerospace, and critical infrastructure.

• Energy generation and storage systems modeling, optimization, and control, with emphasis on reliability, affordability, and national security.

• Experimental platforms for high-temperature, high-pressure energy systems.

• Risk-informed design of energy infrastructure.

Candidates should demonstrate the ability to lead an independent, externally funded research program and contribute to interdisciplinary collaborations. A strong record of peer-reviewed publications, mentorship, and engagement with industry, national laboratories, or large-scale research initiatives is highly desirable. Experience with computational tools (e.g., CFD, FEA, system-level modeling) and/or experimental platforms for energy systems is expected.

Position # 2 - Machine Learning and AI for Energy Systems (Host Department: Computer Science and possibly Electrical and Computer Engineering Department):

An ideal candidate would have extensive experience applying modern machine learning techniques to solving complex problems within the energy systems. The candidate should have a strong foundation in deep learning techniques (including convolutional neural networks, recurrent neural networks and multi-head attention models). An experience with specialized foundation models is a plus.

Areas of interest include but are not limited to:

• Energy market and load/generation forecasting
  , including short- and long-term demand prediction, renewable generation forecasting (solar, wind, hydro) under uncertainty, spatiotemporal modeling for distributed energy systems, energy markets, transfer learning and domain adaptation for data-scarce regions and integration of weather, mobility, and socioeconomic data for predictive modeling.

• Distributed infrastructure resource management, including data-driven modeling and coordination of interdependent infrastructure systems and their subsystems (such as networks of transportation, gas, electricity grid), multi-agent reinforcement learning for distributed coordination and demand response optimization using AI.

• Distributed infrastructure system optimization
  , such as real-time optimization of interdependent infrastructure and energy systems using AI and advanced control methods, hybrid physics-informed and data-driven modeling and control for stability and flexibility.

• Energy system resiliency research, including detection of faults and cyber-attacks, outage prediction under extreme conditions, resilient cybersecurity solutions, and self-healing algorithms.

Candidates should demonstrate not…