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

About the role

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.

Pay

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

Responsibilities

• 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.

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 teaching environment.
• Proven ability to work independently and as part of a collaborative, multidisciplinary, and multi-institutional research team.

Desired Qualifications

• Experience in one or more of the following areas is desirable (candidates are not expected to have prior experience in every area listed):
  • Gyrokinetic turbulence simulations, particularly with CGYRO or a comparable code.
  • Energetic-particle physics or fast-ion transport modeling.
  • FAR3d or related reduced-MHD/gyrofluid simulation tools.
  • Integrated transport modeling using COTSIM, MMM, TRANSP, or related frameworks.
  • L–H/H–L transition physics, pedestal evolution, or core-edge coupling.
  • Analysis and validation using experimental data from tokamak facilities.
  • Machine learning, scientific AI, neural-network surrogate models, physics-informed neural networks, uncertainty quantification, or digital-twin development.
  • High-performance computing and parallel numerical workflows.

Schedule

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.