Research Scientist - Material Modelling
Core
Developing and applying machine learning interatomic potentials (MLIPs) to model atomistic systems and solve problems in materials science and computational chemistry.
Role type
Research Scientist - Material Modelling
Builds
Production-ready ML models for materials simulation and generative modelling
Domain
Deep-tech, AI-driven simulation, Materials Science, Computational Chemistry
Deliverable
production ML models
Required skills
Machine learning interatomic potentials (MLIPs), Python, High-performance computing, Generative models, Large-scale dataset experimentation, Model architecture design
Preferred skills
Experience with MACE or FAIRChem/OCP frameworks, Knowledge of OC20/OC22/Materials Project datasets, Generative models for molecular systems
Technologies
MACE, FAIRChem, OCP, Python
Responsibilities
Develop and apply MLIPs to model atomistic systems; Design and run experiments on large-scale chemistry and materials datasets; Own research workstreams from model development to evaluation; Collaborate to ensure models are robust and reproducible; Work on high-performance computing infrastructure; Communicate research via publications and workshops; Mentor colleagues in computational chemistry or materials ML
Seniority
Individual Contributor (IC), Research-focused
