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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Postdoctoral Research Associate in Control Systems, Artificial Intelligence, and Scientific Machine Learning for Fusion Energy - **Company:** Lehigh University - **Location:** Bethlehem, PA, United States (Remote available) - **Salary:** $65,500.0 - $98,000.0 - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Data Analysis, Artificial Neural Networks, C++ (Programming Language), Computer Programming, Dynamical Systems, Genetic Algorithm, Python (Programming Language), MATLAB, Machine Learning, Tensorflow, Scientific Computating, SciPy, Product Software Implementation Methods, Reinforcement Learning, Digital Twin, High Performance Computing, Pytorch, Transfer Learning, Information Technology, Data Analytics - **Published:** September 3, 2026 - **Apply:** https://www.jofdav.com/jobs/59536807-postdoctoral-research-associate-in-control-systems-artificial-intelligence-and-scientific-machine-learning-for-fusion-energy ## About the Role * 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 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): * Prior research experience in plasma control, tokamak magnetic confinement fusion, or computational fusion science. * 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. * Model Predictive Control (MPC), data-driven control, or hybrid model-based / data-driven controller synthesis (e.g., RL-MPC) for complex dynamical systems. * State estimation, Extended Kalman Filters (EKF), neural observers, physics-informed virtual sensors, or real-time diagnostic mapping. * MATLAB/Simulink integrated simulation workflows, digital twins, or plasma predictive modeling platforms (e.g., COTSIM, TRANSP, SOLPS). * Actuator management, reference governors, constrained control, or active risk management / disruption prevention algorithms. * Nonlinear trajectory optimization, nonlinear programming, or genetic algorithms. * High-performance computing (HPC), parallel numerical workflows, or GPU-accelerated model execution., · A cover letter detailing research experience and interests, career goals, and alignment with the LU-PCG's research areas. ## Description 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., 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 (<1 ms execution time). * 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. 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