Postdoctoral Research Associate in Computational Fusion Plasma Physics, Turbulence, Fast-Ion Transport, and AI-Enabled Modeling at Lehigh University

Job description

The Lehigh University Plasma Control Laboratory invites applications for a Postdoctoral Research Associate position in computational fusion plasma physics. The successful candidate will join the Lehigh University Plasma Control Group (LU-PCG) and contribute to research on turbulent transport, energetic-particle physics, predictive modeling, and AI-enabled digital twins for magnetically confined fusion plasmas in tokamaks. The research will combine high-fidelity simulations, reduced transport modeling, experimental validation, and machine learning. 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, including scientific machine learning, surrogate development, and integrated core-edge simulation and control. This role offers a unique opportunity to work with Professors Eugenio Schuster and Tariq Rafiq in the field of computational fusion plasma physics, engage in cutting-edge research, build a larger and stronger professional network, and gain experience in mentorship and academic service. Position Number : RC1460 Anticipated Salary: $63,480 - $90,000 (based on qualifications and experience) + Benefits. Key Responsibilities The incoming postdoctoral researcher will work on fundamental and applied research in computational fusion plasma physics, turbulence, fast-ion transport, and AI-enabled modeling. The researcher will lead or contribute to key research activities within the LU-PCG, advancing integrated modeling and control for magnetically confined fusion plasmas in tokamaks. Specific responsibilities include: Perform global CGYRO simulations to investigate turbulence spreading and nonlocal transport effects. Conduct multiscale CGYRO simulations to study cross-scale interactions among ion- and electron-scale turbulence. Quantify the effects of electron beta and collisionality on core and pedestal transport, including comparisons between lithium-conditioned and non-lithium plasma conditions. Analyze simulation and experimental results to identify the physical mechanisms governing turbulent transport, fast-ion redistribution, confinement transitions, and pedestal behavior. Develop or apply AI and machine-learning methods for rapid surrogate modeling, model calibration, state estimation, uncertainty quantification, and physics-informed prediction. Contribute to U.S. Department of Energy GENESIS-related AI research, including the development and validation of fast models for integrated core-edge digital twins, control-oriented simulations, and fusion-plasma scenario optimization. Prepare and publish research findings in peer-reviewed journals and present results at scientific conferences and collaborative meetings. Assist in the mentorship of graduate and undergraduate students, providing guidance on computational techniques, data analysis, and project development. Contribute to grant writing, including assisting in the development of proposals and identifying funding opportunities. Participate in departmental seminars, workshops, and other academic events. The precise balance of activities will be determined by the candidate's expertise and the needs of the research program. Required Qualifications Doctoral degree in plasma physics, nuclear engineering, applied physics, computational science, or a closely related field, completed by the start of the appointment. Strong foundation in plasma physics and magnetic-confinement fusion. Demonstrated research experience in computational physics, numerical simulation, or scientific computing, with a track record of publication in peer-reviewed scientific journals. Proficiency in at least one scientific programming language, such as Fortran, Python, MATLAB, C, or C++. Strong writing, verbal, and interpersonal communication skills.. Commitment to fostering an inclusive research and t