Research Associate - Physics-Informed ML for Polymers

University of Sheffield; Sheffield; Híbrido; GBP 40.000 - 42.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

41 days annual leave

Hybrid working

Generous pension

Descripción de la vacante

The University of Sheffield is seeking a Research Associate in Physics-Informed Machine Learning for Polymer Science. You will develop explainable ML models, featurise complex polymer systems, and deliver open-source software for sustainable inverse formulations.

The role is funded for 39 months, hybrid working, and requires a PhD in a quantitative field with strong programming and ML skills.

Formación

• PhD or near completion in a quantitative science (chemistry/physics/CS).
• Experience in programming for scientific computing and ML, with version control (Git).
• Ability to bridge theory and data in multi-disciplinary teams.

Responsabilidades

• Develop explainable ML models to map polymer-solvent thermodynamics.
• Featurise complex polymer architectures and integrate multi-modal data.
• Deliver well-documented open-source software and workflows in Python/Julia.
• Collaborate with the project team and industry partner to validate model predictions.
• Publish high-quality research and present findings at conferences.
• Carry out additional duties commensurate with the role.

Conocimientos

Python

Julia

C++

Git

Explainable AI

Educación

PhD in computational chemistry/physics/CS

Herramientas

Python

Julia

Open-source tools

Molecular simulation

Descripción del empleo

The University of Sheffield is seeking a Research Associate in Physics-Informed Machine Learning for Polymer Science. You will develop explainable ML models, featurise complex polymer systems, and deliver open-source software for sustainable inverse formulations.

The role is funded for 39 months, hybrid working, and requires a PhD in a quantitative field with strong programming and ML skills.