The Lehigh University Plasma Control Laboratory invites applications for a Postdoctoral Research Associate position focusing on control systems engineering, artificial intelligence (AI), and scientific machine learning (SciML) applied to nuclear fusion energy. The successful candidate will join the Lehigh University Plasma Control Group (LU-PCG) and contribute to advanced control synthesis, neural observer development, scenario optimization, and AI-enabled digital twins for magnetically confined fusion plasmas in tokamaks. This position targets experts in control theory, data science, or machine learning who are seeking to apply their expertise to nuclear fusion, as well as researchers with established backgrounds in plasma control.
A key feature of this position is the opportunity to collaborate with major U.S. and international fusion facilities (such as DIII-D, NSTX-U, KSTAR, WEST, and ITER) and contribute to LU-PCG’s research under the U.S. Department of Energy’s (DOE) GENESIS Mission. Research will involve developing fast neural surrogate models, state estimators/virtual sensors, multi-input multi-output (MIMO) closed-loop controllers (MPC, RL, hybrid RL-MPC), and real-time actuator management architectures embedded in MATLAB/Simulink digital-twin environments (COTSIM). This role offers a unique opportunity to work with Professors Eugenio Schuster and Tariq Rafiq in the field of advanced fusion control systems, engage in cutting-edge control/AI research, build a larger and stronger professional network, and gain experience in mentorship and academic service.
Position Number: RC1470
Anticipated Salary: $63,480 - $90,000 (based on qualifications and experience) + Benefits.
Key Responsibilities
The incoming postdoctoral researcher will lead or contribute to key research activities within the LU-PCG, advancing control theory, machine learning algorithms, and integrated simulation workflows for tokamak fusion reactors. Specific responsibilities include:
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Neural Surrogate Modeling: Develop, train, and validate fast neural-network surrogate models (e.g., transport surrogates, edge surrogates, free-boundary MHD surrogates) for real-time predictions and control-oriented execution (<1 ms execution time).
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Advanced Control Synthesis: Synthesize and computationally test model-based (Model Predictive Control - MPC), data-driven (Reinforcement Learning - RL), and hybrid (RL-MPC) multi-input multi-output (MIMO) controllers for kinetic, profile, equilibrium, divertor detachment, and burn regulation.
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State Estimation & Observers: Design and implement state estimators, Extended Kalman Filters (EKF), neural observers, and physics-informed "virtual sensors" for real-time plasma state estimation and boundary/equilibrium reconstruction from limited, noisy diagnostic measurements.
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Scenario Optimization: Develop plasma scenario optimization workflows leveraging nonlinear programming, genetic algorithms, and reinforcement learning for ramp-up, full-discharge, ramp-down, and burning-plasma-transition trajectory generation.
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Actuator Management & Arbitration: Formulate multi-objective actuator management, reference governors, and arbitration strategies to coordinate competing actuators, prevent proximity to instabilities (e.g., NTMs), and ensure machine protection.
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Digital Twin Integration: Integrate neural surrogates, plasma transport solvers, and closed-loop control algorithms into MATLAB/Simulink end-to-end predictive workflows based on LU-PCG’s COTSIM (Control Oriented Tokamak SIMulator) for in silico closed-loop validation.
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Mentorship & Service: Assist in mentoring graduate and undergraduate students in control theory, machine learning, data analysis, and software implementation; assist in grant proposal development.
The precise balance of activities will be determined by the candidate's expertise and the needs of the research program.
Required Qualifications
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Doctoral degree in Control Engineering, Electrical Engineering, Applied Mathematics, Computer Science, Data Science, Mechanical Engineering, Physics, or a closely related quantitative field, completed by the start of the appointment.
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Strong theoretical and practical background in Control Systems Theory (e.g., MIMO control, state-space methods, optimal control, model predictive control, system identification) AND/OR Artificial Intelligence / Machine Learning / Data Science (e.g., deep neural networks, surrogate modeling, reinforcement learning, scientific AI).
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Proficiency in scientific computing languages and environments such as MATLAB/Simulink, Python (PyTorch, TensorFlow, SciPy), or C/C++.
Desired Qualifications
Experience or interest in one or more of the following areas is highly desirable (candidates are not expected to have prior experience in every listed area, and applicants from non-fusion control/AI backgrounds are strongly encouraged to apply):
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Artificial Intelligence (AI), Machine Learning (ML), and Scientific Machine Learning (SciML) applied to physical or engineered systems, including deep neural network surrogate modeling, physics-informed neural networks (PINNs), reinforcement learning (RL), transfer learning, dynamic system surrogates, or uncertainty quantification.
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Model Predictive Control (MPC), data-driven control, or hybrid model-based / data-driven controller synthesis (e.g., RL-MPC) for complex dynamical systems.
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State estimation, Extended Kalman Filters (EKF), neural observers, physics-informed virtual sensors, or real-time diagnostic mapping.
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MATLAB/Simulink integrated simulation workflows, digital twins, or plasma predictive modeling platforms (e.g., COTSIM, TRANSP, SOLPS).
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Actuator management, reference governors, constrained control, or active risk management / disruption prevention algorithms.
Terms of Appointment
This is a full-time, two-year position with the possibility of renewal based on performance and funding availability. The position will start on a mutually agreed-upon date. Compensation will be competitive and commensurate with qualifications and experience, including benefits. Please visit the Postdoctoral Affairs Office website for the compensation policy at Lehigh University.
Application Process
Applicants should submit:
· A cover letter detailing research experience and interests, career goals, and alignment with the LU-PCG’s research areas.
· A curriculum vitae (CV) with a list of publications.
· A research statement (1-2 pages).
· Contact information for three references (name and email).
Applications should be submitted through this website by following the link at the bottom of the page. Please also send a copy of all application materials to Prof. Eugenio Schuster, Director of LU-PCG, via e-mail (schuster@lehigh.edu). Applications will be reviewed on a rolling basis until the position is filled.
About Lehigh University & LU Plasma Control Group
For more than 150 years, Lehigh University has combined outstanding academic and learning opportunities with leadership in fostering innovative research. The institution is among the nation's most selective, highly ranked private research universities. Lehigh’s Department of Mechanical Engineering & Mechanics (MEM) is consistently ranked among the best in the country. Lehigh’s five colleges provide graduate and undergraduate education to approximately 8,000 students. Located in Bethlehem, Pennsylvania, Lehigh University is 80 miles west of New York City and 50 miles north of Philadelphia, providing an accessible and convenient location with an appealing mix of urban and rural lifestyles. The Lehigh Valley International Airport is just six miles from campus. Lehigh Valley cities and towns are regularly listed as among the best places to live in the country.
The Lehigh University Plasma Control Group, directed by Professors Eugenio Schuster and Tariq Rafiq, conducts research at the intersection of theory-based modeling, predictive simulation, artificial intelligence, and plasma control. Group members have backgrounds in plasma physics, applied mathematics, computational science, machine learning, and control engineering. Current collaborations involve major fusion facilities and research programs, including DIII-D (San Diego, CA, USA), NSTX-U (Princeton, NJ, USA), KSTAR (Daejeon, South Korea), WEST (CEA Cadarache, France), ITER (Saint- Paul-lez-Durance, France), and U.S. Department of Energy national laboratories.
Institutional Statements & Standard Policy Language
Lehigh University offers a vibrant and inclusive work environment that fosters professional growth, personal development, and a strong sense of community. With a commitment to diversity, equity, and inclusion, we value the unique perspectives and experiences of our employees. Competitive benefits, including comprehensive healthcare plans, tuition remission, and retirement savings opportunities, ensure your well-being and long-term success. Lehigh University is an equal opportunity employer and does not discriminate. We are committed to a culturally and intellectually diverse community and we seek qualified candidates to contribute to the university’s mission.
Persons with disabilities who anticipate needing an accommodation for any part of the interview or hiring process may contact Lehigh's Accommodations Specialist.
Successful completion of standard background checks including but not limited to: social security verification, education verification, national criminal background checks, motor vehicle checks, PATCH, FBI fingerprinting, Child Abuse Clearance and credit history based upon the requirements of the position.
Only complete applications will be considered therefore please complete the application in its entirety. Once the posting is removed from the website applications may no longer be allowed to be completed.
