ML Protein Assembly Scientist – CoreShell Design

University of Strathclyde; Glasgow; On-site; GBP 42,000 - 52,000; Full time

Job summary

The University of Strathclyde is seeking a Research Associate to lead the machine-learning component of the CoreShell Fibre Foundry programme in the ARIA-funded project. The role involves building surrogate models that link protein sequences to experimental outcomes and guiding design-build-test cycles within an interdisciplinary team of computational chemists and engineers.

You will design features, develop Python workflows, and communicate results to collaborators.

Qualifications

• PhD in computational chemistry, chemical physics, molecular modelling, bioinformatics, machine learning, computational biology, materials informatics or a closely related discipline is required.
• Experience developing and evaluating machine-learning models is essential.
• Strong Python and scientific computing skills are required.
• Ability to work independently and as part of a collaborative team.

Responsibilities

• Develop machine-learning and active-learning models for predictive protein-assembly design.
• Define suitable molecular descriptors, input features and prediction targets.
• Analyse sequence, simulation and experimental datasets.
• Prioritise candidate protein sequences for simulation and experimental validation.
• Develop robust, documented Python workflows and share results with collaborators.
• Contribute to project meetings, milestone reports, and publications.

Skills

Machine-learning

Python

Data analysis

Active learning

Bayesian optimization

Education

PhD in a relevant field

Tools

Python workflows

Molecular descriptors

Job description

The University of Strathclyde is seeking a Research Associate to lead the machine-learning component of the CoreShell Fibre Foundry programme in the ARIA-funded project. The role involves building surrogate models that link protein sequences to experimental outcomes and guiding design-build-test cycles within an interdisciplinary team of computational chemists and engineers.

You will design features, develop Python workflows, and communicate results to collaborators.