AI Materials Scientist: Graph ML for Discovery + Equity

Quantum Formatics; Cambridge; Hybrid; GBP 89,000 - 134,000; Full time

Benefits offered by this job

Hybrid work environment at The Engine

Competitive salary based on experience

Health, dental, and vision insurance

401(k)

Unlimited PTO

Relocation reimbursement

Job summary

Quantum Formatics is seeking a Computational Scientist to advance an AI-accelerated materials discovery pipeline. You will develop graph neural networks for materials property prediction and integrate ML interatomic potentials while benchmarking against experimental data.

You will collaborate with the Lead Scientist and experimental teams, mentoring future members as the team grows, and help shape the computational platform direction.

Qualifications

• Ph.D. with a strong focus on atomistic modeling or AI-accelerated materials discovery.
• At least 3 years of research experience in AI for materials discovery or related field.
• Experience with graph neural networks for materials property prediction.
• Familiarity with DFT and MD simulations; proficiency in Python and PyTorch.
• Experience training ML models for scientific applications and analyzing large datasets.
• Ability to work in HPC environments and manage computational workflows at scale.

Responsibilities

• Develop and train graph neural network models for materials property prediction.
• Evaluate and integrate machine learning interatomic potentials; benchmark against experimental data.
• Collaborate with the Lead Scientist and experimental teams to close the loop prediction–synthesis–characterization–model improvement.

Skills

Graph neural networks

Python

PyTorch

Density functional theory

Molecular dynamics

HPC workflows

Data analysis

Collaboration skills

Education

Ph.D. in Physics, Chemistry, Materials Science, Computer Science, or closely related field

Tools

Fortran

C

Job description

Quantum Formatics is seeking a Computational Scientist to advance an AI-accelerated materials discovery pipeline. You will develop graph neural networks for materials property prediction and integrate ML interatomic potentials while benchmarking against experimental data.

You will collaborate with the Lead Scientist and experimental teams, mentoring future members as the team grows, and help shape the computational platform direction.