Postdoctoral Research Associate in Control Systems, Artificial Intelligence, and Scientific Machine Learning for Fusion Energy

About the role

The Lehigh University Plasma Control Laboratory seeks a Postdoctoral Research Associate to work 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), collaborate with major U.S. and international fusion facilities (DIII‑D, NSTX‑U, KSTAR, WEST, ITER) under the DOE GENESIS Mission, and contribute to the development of fast neural surrogate models, state estimators/virtual sensors, MIMO closed‑loop controllers (MPC, RL, hybrid RL‑MPC), actuator management architectures, and MATLAB/Simulink digital‑twin workflows (COTSIM). The role also includes mentorship of graduate and undergraduate students, assistance with grant proposals, and dissemination of results in top‑tier journals and conferences.

Responsibilities

• Neural Surrogate Modeling: Develop, train, and validate fast neural‑network surrogate models (transport surrogates, edge surrogates, free‑boundary MHD surrogates) for real‑time predictions and control‑oriented execution.
• Advanced Control Synthesis: Synthesize and computationally test model‑based (MPC), data‑driven (RL), and hybrid (RL‑MPC) multi‑input multi‑output (MIMO) controllers for kinetic, profile, equilibrium, divertor detachment, and burn regulation.
• 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.
• 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.
• 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.
• 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 for in silico closed‑loop validation.
• Publication & Dissemination: Prepare and publish research findings in top‑tier peer‑reviewed scientific journals and present results at national and international control, AI, and fusion conferences.
• Mentorship & Service: Assist in mentoring graduate and undergraduate students in control theory, machine learning, data analysis, and software implementation; assist in grant proposal development.

Requirements

• 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.
• 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).
• Willingness to work on interdisciplinary problems at the intersection of AI, control, and physical sciences.
• Demonstrated ability to conduct original research with a strong track record of publications in peer‑reviewed scientific journals or premier conference proceedings.
• Proficiency in scientific computing languages and environments such as MATLAB/Simulink, Python (PyTorch, TensorFlow, SciPy), or C/C++.
• Strong writing, verbal, and interpersonal communication skills.
• Commitment to fostering an inclusive research and teaching environment.
• Proven ability to work independently and as part of a collaborative, multidisciplinary, and multi‑institutional research team.
• Desired (but not required) experience or interest in: prior plasma control/tokamak fusion research; AI/ML/SciML applied to physical systems (deep neural surrogate modeling, PINNs, RL, transfer learning, dynamic system surrogates, uncertainty quantification); MPC, data‑driven, or hybrid RL‑MPC controller synthesis; state estimation, EKF, neural observers, physics‑informed virtual sensors, real‑time diagnostic mapping; MATLAB/Simulink integrated simulation workflows, digital twins, or plasma predictive modeling platforms (COTSIM, TRANSP, SOLPS); actuator management, reference governors, constrained control, active risk management/disruption prevention; nonlinear trajectory optimization, nonlinear programming, genetic algorithms; HPC, parallel numerical workflows, or GPU‑accelerated model execution.

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.

Benefits

Competitive benefits including comprehensive healthcare plans, tuition remission, and retirement savings opportunities.

Pay

Anticipated salary: $63,480 – $90,000 (based on qualifications and experience) + Benefits.