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

Princeton University; Princeton (NJ); On-site; USD 94,000 - 149,000; Full time

Job summary

Princeton Plasma Physics Laboratory (PPPL) seeks an Associate Computational Scientist to advance modeling capabilities for low-temperature plasmas. The role centers on HPC, molecular and kinetic modeling, and ML-accelerated codes to support research and design of plasma devices.

The candidate will work with LTP-PIC software, contribute to MPI/OpenMP/OpenACC frameworks, and apply Python, NumPy, and PyTorch. The position includes collaboration with academia and industry and participation in

Qualifications

• Ph.D. in Physics, Engineering or related field.
• Minimum 3 years of professional experience in an academic, scientific, or R&D environment is preferred.
• Proven track record of publishing original results in peer‑reviewed journals.

Responsibilities

• Ongoing development and maintenance of the LTP-PIC software and assisting users from academia and industry (50%).
• Defining and delivering on A.I. projects for low-temperature plasmas (20%).
• Modeling low-pressure discharges for industry partners (20%).
• Publishing scientific results and presenting at major international conferences (10%).

Skills

High-performance computing

Machine learning for science

Python (NumPy, PyTorch)

MPI

OpenMP/OpenACC

Education

Ph.D. in Physics, Engineering or related field

Tools

LTP-PIC software

Job description

Job Type

Full-Time

Overview

The Princeton Plasma Physics Laboratory (PPPL) is seeking to appoint a Associate 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 magnetron, 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 magnetron, 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.

This position is part of PPPL’s prestigious Strategic Science Initiative (SSI) Fellowship Program, which provides exceptional early-career researchers with the opportunity to lead innovative research and help advance new scientific directions at the Laboratory. As an SSI Fellow, the successful candidate will serve as an Associate Research Scientist and contribute to PPPL’s research in applied materials and electromanufacturing, with a focus on advancing computational modeling, high-performance computing, and machine learning for low-temperature plasma science and technology.

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!

Responsibilities

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 (50%).
• Defining and delivering on A.I. projects for low-temperature plasmas (20%).
• Modeling low-pressure discharges for industry partners (20%).
• Publishing scientific results and dissemination at major international conferences (10%).

Qualifications

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 is preferred.
• A proven track record of publishing original results in peer-reviewed scientific journals.
• A proven track record of developing high-performance codes.
• Demonstrated collaborative experience within academia and with industry.

Working Conditions

• Regular office hours.
• 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.

Princeton University is an Equal Opportunity Employer 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 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.

Standard Weekly Hours

40.00

Eligible for Overtime

No

Benefits Eligible

Yes

Probationary Period

180 days

Essential Services Personnel (see policy for detail)

No

Physical Capacity Exam Required

No

Valid Drivers License Required

No

Salary Range

$93,500 to $149,300

Equal employment opportunity, including veterans and individuals with disabilities.

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