Computational Scientist: 2D Materials & Low-Temp Plasma

Princeton Plasma Physics Laboratory (PPPL); Princeton (NJ); On-site; USD 111,000 - 178,000; Full time

Job summary

The Princeton Plasma Physics Laboratory (PPPL) seeks a Computational Scientist to advance modeling capabilities and physics research in low-temperature plasmas for processing two‑dimensional materials. The role emphasizes DFT/ab initio MD modeling and ML potentials to accelerate simulations and guide design.

Candidates should have experience with 2D material properties, MD/band gap calculations, and a track record of collaborating with academia and industry.

Qualifications

• Ph.D. in Physics, Engineering or related field; 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; optimize processing techniques.
• Contribute to ongoing project on processing of two-dimensional materials based on transition metal dichalcogenides.
• Defining and delivering AI projects for low-temperature plasmas.
• Proposal ideation and preparation for research funding.
• Modeling plasma processing for industry partners.
• Publish scientific results and disseminate at major international conferences.

Skills

DFT modeling

Molecular dynamics

2D materials

Plasma processing

Python/Scientific computing

Communication skills

Education

Ph.D. in Physics, Engineering or related field

Tools

VASP

CP2K

Gaussian

Quantum ESPRESSO

LAMMPS

Job description

The Princeton Plasma Physics Laboratory (PPPL) seeks a Computational Scientist to advance modeling capabilities and physics research in low-temperature plasmas for processing two‑dimensional materials. The role emphasizes DFT/ab initio MD modeling and ML potentials to accelerate simulations and guide design.

Candidates should have experience with 2D material properties, MD/band gap calculations, and a track record of collaborating with academia and industry.