Postdoctoral Research Associate in Control Systems, Artificial Intelligence, and Scientific Machine Learning for Fusion Energy
Job Location:
Bethlehem - South Africa
Yearly Salary:
Posted:
Application Deadline:
Vacancies:
Job Summary
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-PCGs research under the U.S. Department of Energys (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: $63480 - $90000 (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 (<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-PCGs 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 by the candidates expertise and the needs of the research program.
Required Qualifications
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.
Terms of 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. Compensation will be competitive and commensurate with qualifications and experience including benefits. Please visit the Postdoctoral Affairs Office website for the compensation policy at Lehigh University.
Application Process
Applicants should submit:
A cover letter detailing research experience and interests career goals and alignment with the LU-PCGs research areas.
A curriculum vitae (CV) with a list of publications.
A research statement (1-2 pages).
Contact information for three references (name and email).
Applications should be submitted through this website by following the link at the bottom of the page. Please also send a copy of all application materials to Prof. Eugenio Schuster Director of LU-PCG via e-mail (). Applications will be reviewed on a rolling basis until the position is filled.
About Lehigh University & LU Plasma Control Group
For more than 150 years Lehigh University has combined outstanding academic and learning opportunities with leadership in fostering innovative research. The institution is among the nations most selective highly ranked private research universities. Lehighs Department of Mechanical Engineering & Mechanics (MEM) is consistently ranked among the best in the country. Lehighs five colleges provide graduate and undergraduate education to approximately 8000 students. Located in Bethlehem Pennsylvania Lehigh University is 80 miles west of New York City and 50 miles north of Philadelphia providing an accessible and convenient location with an appealing mix of urban and rural lifestyles. The Lehigh Valley International Airport is just six miles from campus. Lehigh Valley cities and towns are regularly listed as among the best places to live in the country.
Institutional Statements & Standard Policy Language
Lehigh University offers a vibrant and inclusive work environment that fosters professional growth personal development and a strong sense of community. With a commitment to diversity equity and inclusion we value the unique perspectives and experiences of our employees. Competitive benefits including comprehensive healthcare plans tuition remission and retirement savings opportunities ensure your well-being and long-term success. Lehigh University is an equal opportunity employer and does not discriminate. We are committed to a culturally and intellectually diverse community and we seek qualified candidates to contribute to the universitys mission.
Persons with disabilities who anticipate needing an accommodation for any part of the interview or hiring process may contact Lehighs Accommodations Specialist.
The duties of the position do not allow for a fully remote work option; the employee in this position will be required to work at Lehigh University (on-campus) or at one of the LU-PCGs collaborating fusion facilities (off-campus).
This position works with minors.
Successful completion of standard background checks including but not limited to: social security verification education verification national criminal background checks motor vehicle checks PATCH FBI fingerprinting Child Abuse Clearance and credit history based upon the requirements of the position.
Only complete applications will be considered therefore please complete the application in its entirety. Once the posting is removed from the website applications may no longer be allowed to be completed.
Required Experience:
IC
