Computational Research Scientist in the Area of Low-Temperature Low-Pressure Processing Plasma

The Princeton Plasma Physics Laboratory (PPPL) is seeking to appoint a Computational Scientist to contribute to the advancement of modeling capabilities and physics research pertaining to low-temperature plasmas and associated technologies. The primary responsibility of this position involves conducting and facilitating computational modeling of authentic low-temperature plasma devices, particularly those operating at low pressure, for the purposes of scientific discovery and engineering design. The successful candidate will achieve this objective through the application of high-performance computing (HPC) best practices, advanced mathematical research into novel algorithms, and the utilization of machine learning techniques for code acceleration and the development of reduced-order "surrogate" models.

This will require working with and maintaining the LTP-PIC software, a particle-in-cell (PIC) software package developed at PPPL for these purposes. The candidate should have strong familiarity with compiler level languages, accelerated computing (preferably with OpenMP or OpenACC) and distributed computing (MPI). A thorough understanding of the particle-in-cell algorithm, its inherent limitations, and potential avenues for performance enhancement is also required. Furthermore, the candidate should demonstrate substantial practical knowledge of low-pressure capacitively and inductively coupled discharges employed in plasma processing, coupled with a proven track record of kinetic modeling of such discharges.

The candidate should be familiar with machine-learning principles for science, including generative A.I. and surrogate models, especially of convolutional and recurrent neural networks. Familiarity with Python, NumPy and PyTorch will be essential for this position.

The computational tools developed will be instrumental in studies of capacitively-coupled plasmas and partially magnetized plasma sources, as well as the fundamental understanding of anomalous transport and plasma turbulence within these devices. The software will also be disseminated to the broader academic and industrial communities, necessitating strong interpersonal and communication skills to cultivate these relationships.

Finally, this role will encompass the conceptualization and preparation of novel proposal ideas to secure funding for future research projects.

A U.S. Department of Energy National Laboratory managed by Princeton University, the Princeton Plasma Physics Laboratory (PPPL) is tackling the world’s toughest science and technology challenges using plasma, the fourth state of matter. With more than 70 years of history, PPPL is a leader in the science and engineering behind the development of fusion energy, a potentially limitless energy source. PPPL is also using its expertise to advance research in the areas of microelectronics, quantum sensors and devices, and sustainability sciences. Whether it be through science, engineering, technology or professional services, every team member has an opportunity to contribute to our mission and vision. Come join us!

Core Duties: 

• The candidate will be responsible for ongoing development and maintenance of the LTP-PIC software, as well as assisting users from academia and industry (20%).
• Defining and delivering on A.I. projects for low-temperature plasmas (40%).
• Proposal ideation and preparation (10%).
• Modeling low-pressure discharges for industry partners (20%).
• Publishing scientific results and dissemination at major international conferences (10%). 

Education and Experience: 

• Ph.D. in Physics, Engineering or a related field with core training in low-temperature plasma physics and high-performance computing.
• Minimum 3 years of professional experience in an academic, scientific, or R&D environment.
• A proven track record of publishing original results in peer-reviewed scientific journals.
• Demonstrated collaborative experience within academia and with industry.

Knowledge, Skills and Abilities: 

• Kinetic plasma theory, plasma waves and instabilities, plasma turbulence and transport.
• Knowledge of low-temperature plasma devices and relevant physics.
• Theoretical and computational knowledge of the particle-in-cell method.
• High-performance computing, including MPI, OpenMP and GPU programming (OpenACC experience is desirable).
• Experience with machine-learning techniques, including convolutional neural-networks, surrogate models and generative AI more broadly.
• Familiar with Python programming, including NumPy, CuPy and PyTorch. 

Working Conditions: 

• Regular office hours. 

Princeton University is an Equal Opportunity and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law.

The University considers factors such as (but not limited to) scope and responsibilities of the position, candidate's qualifications, work experience, education/training, key skills, market, collective bargaining agreements as applicable, and organizational considerations when extending an offer. The posted salary range represents the University's good faith and reasonable estimate for a full-time position; salaries for part-time positions are pro-rated accordingly.

If the salary range on the posted position shows an hourly rate, this is the baseline; the actual hourly rate may be higher, depending on the position and factors listed above.

The University also offers a comprehensive benefit program to eligible employees. Please see this link for more information.

Please be aware that the Department of Energy (DOE) prohibits DOE employees and contractors from participation in certain foreign government talent recruitment programs. All PPPL employees are required to disclose any participation in a foreign government talent recruitment program and may be required to withdraw from such programs to remain employed under the DOE Contract.