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

Job description

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: 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 ( 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. 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 (Control Oriented Tokamak SIMulator) 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. The precise balance of activities will be determined b