Computational Research Scientist (Two-Dimensional Materials and Low-Pressure Processing...

University of Georgia- FACS; Princeton, Northern (NJ, KY); On-site; USD 90,000 - 130,000; Full time

Job summary

Princeton Plasma Physics Laboratory (PPPL) seeks a Computational Scientist to advance modeling capabilities and physics research for low-temperature plasmas used in processing 2D materials.

The role involves DFT/ab initio MD, ML potentials for MD acceleration, and band gap calculations of 2D materials, with strong emphasis on collaboration and proposal development.

Qualifications

• Experience with density functional theory (DFT) and molecular dynamics simulations in materials science.
• Proven ability to model 2D material properties and connect to experimental data.
• Strong background in plasma processing of materials and discharges.

Responsibilities

• Conduct and facilitate computational modeling of 2D material processing using low-temperature plasmas.
• Apply DFT/ab initio MD and ML potentials to enable discovery and design of processes.
• Communicate results to academic and industrial communities and contribute to proposal development.

Skills

MD simulations

DFT modeling

ab initio MD

Machine learning potentials

band gap calculations

2D materials

plasma processing

communication

proposal writing

Tools

VASP

Quantum ESPRESSO

LAMMPS

Job description

Job Type Full-Time

Overview

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 using low-temperature plasmas for processing of two-dimensional materials and associated technologies. The primary responsibility of this position involves conducting and facilitating computational modeling of processing of 2D materials by low-temperature plasma for the purposes of scientific discovery and engineering design of these processes. The successful candidate will achieve this objective through the application of density functional theory (DFT) modeling of these processes using ab initio molecular dynamics (MD), and the utilization of machine learning potentials for MD acceleration. The candidate should have strong practical familiarity with MD simulations and band gap structure calculations of 2D material properties and connection to experimental measurements. Furthermore, the candidate should demonstrate substantial practical knowledge of 2D material properties and techniques employed in plasma processing, coupled with a proven track record of modeling of such discharges. The results will 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.