ML for Amorphous Materials: Simulation Scientist

University of Cambridge; Cambridge; On-site; GBP 38,000 - 48,000; Full time

Job summary

The University of Cambridge’s Cavendish Laboratory invites a postdoctoral researcher to develop and apply machine learning methods for the atomistic simulation of materials, with a focus on amorphous and disordered systems, in the Theory of Condensed Matter group led by Dr Christoph Schran.

The role covers generating reference data, training and validating ML interatomic potentials, and running large-scale molecular dynamics on national HPC facilities, with opportunities to shape project

Qualifications

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

Responsibilities

• Develop and apply ML models for amorphous materials.
• Generate reference data, train and validate models.
• Run large-scale molecular dynamics simulations on national HPC facilities.
• Collaborate with the Theory of Condensed Matter group and across the Lennard-Jones Centre.

Skills

Machine learning interatomic Potentia

Python

PyTorch

Molecular dynamics

Education

PhD in physics, chemistry, materials science or related

Tools

High-performance computing

Density Functional Theory (DFT)

Job description

The University of Cambridge’s Cavendish Laboratory invites a postdoctoral researcher to develop and apply machine learning methods for the atomistic simulation of materials, with a focus on amorphous and disordered systems, in the Theory of Condensed Matter group led by Dr Christoph Schran.

The role covers generating reference data, training and validating ML interatomic potentials, and running large-scale molecular dynamics on national HPC facilities, with opportunities to shape project