Research Associate in Physics-Informed Machine Learning for Polymer Science

University of Sheffield; Sheffield; On-site; GBP 42,000 - 54,000; Full time

Benefits offered by this job

38 days annual leave

Pension scheme

Discounts at shops/cinemas

Cycle to work

Development and mentoring programs

Wellbeing support

Job summary

University of Sheffield invites applications for a fully funded 39-month position in Surface engineering & tribology for silicone-free consumer products. You will develop explainable machine learning models to decode polymer-solvent thermodynamics and map soft-matter physics to real-world applications.

Candidates should hold a PhD (or near completion) in a relevant quantitative field and have programming experience (Python, Julia, or C++) with Git for version control.

Qualifications

• PhD in computational chemistry, cheminformatics, physics, chemical engineering, applied mathematics, computer science, or closely related quantitative discipline.

Responsibilities

• Develop explainable ML models (symbolic regression, Bayesian optimisation, genetic algorithms).
• Featurise complex polymer architectures for ML models and analyse multi-modal datasets.
• Create open-source cheminformatics tools in Python/Julia for design and discovery.

Skills

Python

Julia

C++

Git

Machine learning

Education

PhD or near completion in a quantitative field

Tools

Open-source cheminformatics

Python/Julia for scientific computing

Job description

Job description:

We have an opening for a fully funded 39-month position at the University of Sheffield in the field of Surface engineering & tribology for silicone-free consumer products, funded by EPSRC.

The project aims to decode the fundamental polymer physics/chemistry of lubrication and slip planes across complex, heterogeneous surfaces, using explainable machine learning and materials informatics to rationally design sustainable, high-performance formulations.

You will be responsible for developing explainable machine learning models (symbolic regression, Bayesian optimisation, and genetic algorithms) to decode polymer-solvent thermodynamics and map soft-matter physics to real world applications. The position involves featurizing complex polymer architectures for ML models, analysing multi-modal experimental datasets, and creating open-source cheminformatics tools in Python/Julia for sustainable inverse formulation design and AI-driven materials discovery.

You will have a PhD degree (or near completion) in computational chemistry, cheminformatics, physics, chemical engineering, applied mathematics, computer science, or a closely related quantitative discipline. You will also have experience in programming (e.g., Python, Julia or C++) for scientific computing, data analysis, or machine learning, including version control (Git).

What We Offer:

• 38 days annual leave (including closure days and Bank Holidays), with the possibility to purchase up to ten additional days.
• Up to 5 days of paid time off to cover emergency caring responsibilities.
• A highly competitive pension scheme.
• Discounts at participating shops and cinemas, and a cycle to work scheme.
• A commitment to your development, with programmes, learning modules and mentoring schemes.
• An inclusive and diverse workplace.
• A commitment to your wellbeing, with confidential emotional support.

Further information can be found at https://www.sheffield.ac.uk/jobs/benefits

We are committed to exploring flexible working opportunities which benefit the individual and University.

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

We build teams of people from different heritages and lifestyles from across the world, whose talent and contributions complement each other to greatest effect. We believe diversity in all its forms delivers greater impact through research, teaching and student experience.