Postdoc: ML Interatomic Potentials for Amorphous Materials

University of Cambridge; Cambridge; On-site; GBP 38,000 - 46,000; Full time

Job summary

The University of Cambridge invites a postdoctoral researcher to develop and apply machine learning methods to atomistic simulations of materials, with emphasis on amorphous and disordered systems. Based in the Theory of Condensed Matter group at the Cavendish Laboratory, under Dr Christoph Schran.

You will generate reference data, train and validate ML interatomic potentials, and run large-scale MD on national HPC facilities, with scope to influence the project and collaborate across the

Qualifications

• PhD in physics, chemistry, materials science or related discipline.
• Strong background in ML interatomic potentials: development, training and validation.
• Experience with Python and ML frameworks (e.g., PyTorch).
• Experience with amorphous or disordered materials is required.

Responsibilities

• Develop and apply ML models for amorphous materials.
• Generate reference data and validate models.
• Run large-scale MD simulations on HPC resources.
• Collaborate within Theory of Condensed Matter and across the Lennard-Jones Centre.

Skills

Machine learning

Python

PyTorch

HPC

GPU computing

Education

PhD in physics, chemistry, materials science

Tools

Density Functional Theory

Job description

The University of Cambridge invites a postdoctoral researcher to develop and apply machine learning methods to atomistic simulations of materials, with emphasis on amorphous and disordered systems. Based in the Theory of Condensed Matter group at the Cavendish Laboratory, under Dr Christoph Schran.

You will generate reference data, train and validate ML interatomic potentials, and run large-scale MD on national HPC facilities, with scope to influence the project and collaborate across the