Computational Scientist: 2D Materials & Low-Temp Plasma

U.S. Fusion Energy; Princeton (CA); On-site; USD 90,000 - 130,000; Full time

Job summary

Princeton Plasma Physics Laboratory (PPPL) seeks a Computational Research Scientist to advance modeling capabilities for processing two-dimensional materials using low-temperature plasmas. The role emphasizes ab initio MD, DFT calculations, and machine-learning potentials to accelerate research and inform design of processing technologies.

The successful candidate will publish results, collaborate across academia and industry, and contribute to proposal ideas to secure future funding.

Qualifications

• 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.

Responsibilities

• Application of ab initio molecular dynamics to low-temperature plasmas for processing of two-dimensional materials and associated technologies.
• The capabilities will be deployed for optimization of processing techniques of these materials.
• The candidate will be responsible for contributing to ongoing project on processing of two-dimensional materials based on transit metal dichalcogenides (50%).
• Defining and delivering on A.I. projects for low-temperature plasmas (20%).
• Proposal ideation and preparation (10%).
• Modeling plasma processing for industry partners (20%).
• Publishing scientific results and dissemination at major international conferences (10%).

Skills

Interpersonal skills

Communication skills

Education

Ph.D. in Physics/Engineering or related field

Tools

First-principles methods

Density Functional Theory (DFT)

Molecular Dynamics (MD)

Job description

Princeton Plasma Physics Laboratory (PPPL) seeks a Computational Research Scientist to advance modeling capabilities for processing two-dimensional materials using low-temperature plasmas. The role emphasizes ab initio MD, DFT calculations, and machine-learning potentials to accelerate research and inform design of processing technologies.

The successful candidate will publish results, collaborate across academia and industry, and contribute to proposal ideas to secure future funding.