Remote MLFF Chemist PFAS Materials Discovery

SandboxAQ is seeking a Computational Chemist to lead machine learned force fields for PFAS substitution in the Chem Sim group. The role focuses on generating training data via high-throughput quantum chemistry, training ML potentials, and deploying them in scalable workflows.

You will design campaigns, validate models, and extend ML potentials to complex chemistries relevant to PFAS research, contributing to semiconductor materials discovery and environmental safety goals.

We would love to welcome a new Remote MLFF Chemist for PFAS Materials Discovery to our team in United States.

Full responsibilities and requirements are described in the listing above.

Learn more about the Remote MLFF Chemist for PFAS Materials Discovery role in the description above.

We appreciate your interest in this position.

Join SandboxAQ and contribute to our ongoing work.

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