Research Associate in Physics-Informed Machine Learning for Polymer Science

The University of Sheffield; Sheffield; On-site; GBP 40,000 - 42,000; Full time

Benefits offered by this job

Hybrid working opportunities

Generous pension

41 days annual leave (pro rata)

Job summary

The University of Sheffield invites applications for a fully funded 39‑month position in Surface engineering & tribology for silicone-free consumer products. The project decodes polymer physics and uses explainable machine learning to design sustainable formulations, with strong collaboration across disciplines.

You will develop symbolic regression, Bayesian optimisation, and genetic algorithms, featurize polymer architectures, and deliver open-source tools in Python/Julia, bridging theory with

Qualifications

• PhD degree (or near completion) in computational chemistry, cheminformatics, physics, chemical engineering, applied mathematics, computer science, or closely related quantitative discipline.
• Programming experience (Python, Julia or C++) for scientific computing with Git version control.
• Ability to quickly learn, integrate, and apply new domain concepts across physical science and computation.
• Track record of high-quality research outputs (peer‑reviewed publications).
• Ability to work effectively in multi-disciplinary teams and communicate complex computational concepts.

Responsibilities

• Algorithm development: develop and benchmark explainable ML models to predict polymer–solvent interactions.
• Data integration and featurization: link polymer architectures to bulk properties.
• Open-source software delivery: build, document, and maintain computational tools in Python/Julia via GitHub.
• Interdisciplinary collaboration: work with the project team to validate model predictions.
• Dissemination: prepare manuscripts for publication and present findings at conferences.
• Carry out other duties commensurate with the grade.

Skills

Python

Julia

C++

Git

Explainable AI

Machine learning

Data analysis

Teamwork

Problem solving

Polymers

Education

PhD in computational chemistry/related field

Tools

Python

Julia

Git

SMILES/BigSMILES

Molecular dynamics/DFT

Job description

The University of Sheffield is a remarkable place to work. Our people are at the heart of everything we do. Their diverse backgrounds, abilities and beliefs make Sheffield a world-class university.

We offer a fantastic range of benefits including a highly competitive annual leave entitlement (with the ability to purchase more), a generous pensions scheme, flexible working opportunities, a commitment to your development and wellbeing, a wide range of retail discounts, and much more. Find out more about our benefits (opens in a new window) and join us to become part of something special.

A fully funded 39 month position is available 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, analysing multi-modal experimental datasets, and creating open-source cheminformatics tools in Python/Julia for sustainable inverse formulation design and AI-driven materials discovery. The work is a highly collaborative project between Dr Grant Hill and colleagues within the School of Mathematical and Physical Sciences, and an industrial partner. The project requires a high degree of independence, strong computational skills, and the initiative to bridge theoretical polymer chemistry with experimental data.

Main Duties And Responsibilities

• Algorithm development: Develop and benchmark explainable machine learning models to predict how polymers interact with solvents.
• Data integration & featurization: Process and featurize complex polymer architectures to link molecular structure with bulk properties.
• Open-source software delivery: Build, document and maintain well-structured, open-source computational tools and workflows in Python/Julia, releasing them via GitHub.
• Interdisciplinary collaboration: Work closely with the whole project team to validate model predictions against real-world data.
• Dissemination: Prepare high-quality manuscripts for peer-reviewed publication and present research findings at national and international conferences.
• Carry out other duties, commensurate with the grade and remit of the post.

Person Specification

Our diverse community of staff and students recognises the unique abilities, backgrounds, and beliefs of all. We foster a culture where everyone feels they belong and are respected. Even if your past experience doesn't match perfectly with this role's criteria, your contribution is valuable, and we encourage you to apply. Please ensure that you reference the application criteria in the application statement when you apply.

Criteria

A PhD degree (or near completion) in computational chemistry, cheminformatics, physics, chemical engineering, applied mathematics, computer science, or a closely related quantitative discipline.

Essential

Application/interview Experience in programming (e.g., Python, Julia or C++) for scientific computing, data analysis, or machine learning, including version control (Git).

Essential

Application/interview Ability to quickly learn, integrate, and apply new domain-specific concepts across physical science and computational disciplines.

Essential

Application/interview Track record of high-quality research outputs (e.g., peer-reviewed publications) appropriate to career stage.

Essential

Application/interview Ability to work effectively within multi-disciplinary teams and communicate complex computational concepts to diverse audiences.

Essential

Application/interview Strong problem solving skills.

Essential

Application/interview Direct experience with soft matter, polymer physics, physical chemistry, or chemical thermodynamics.

Desirable

Application/interview Experience with explainable AI (XAI) or optimisation techniques (such as symbolic regression, Bayesian optimisation, or genetic algorithms).

Desirable

Application/interview Familiarity with cheminformatics tools, polymer representations (SMILES / BigSMILES), or molecular simulation (e.g., molecular dynamics or DFT).

Desirable

Application/interview Track record of developing, documenting, and releasing open-source software tools or computational packages.

Desirable

Application/interview Experience working alongside industrial partners or translating academic research into industrial applications.

Further Information

Grade

7

Salary

£39,560 - £41,885

Work arrangement

Full-time

Duration

39 months

Line manager

Dr Grant Hill, Senior Lecturer in Theoretical Chemistry

Direct reports

N/A

Our website

https://sheffield.ac.uk/mps

For informal enquiries

contact Dr Grant Hill, Senior Lecturer in Theoretical Chemistry: [e-mail]

Next steps in the recruitment process

It is anticipated that the selection process will take place in December 2026. This will consist of a presentation, an interview, and a discussion with questions & answers. We plan to let candidates know if they have progressed to the selection stage within 2 weeks of the closing date. If you need any support, equipment or adjustments to enable you to participate in any element of the recruitment process you can contact [e-mail].

Our vision and strategic plan

We are the University of Sheffield. This is our vision: sheffield.ac.uk/vision (opens in new window).

What We Offer

• A minimum of 41 days annual leave including bank holiday and closure days (pro rata) with the ability to purchase more.
• Flexible working opportunities, including hybrid working for some roles.
• Generous pension scheme.
• A wide range of discounts and rewards on shopping, eating out and travel.
• A variety of staff networks, providing opportunities for social interaction, peer support and personal development (for example, Race Equality, LGBT+, Women’s and Parent’s networks).
• Recognition Awards to reward staff who go above and beyond in their role.
• A commitment to your development access to learning and mentoring schemes; integrated with our Academic Career Pathways
• A range of generous family-friendly policies
• paid time off for parenting and caring emergencies
• support for those going through the menopause
• paid time off and support for fertility treatment
• and more

More details can be found on our benefits page: sheffield.ac.uk/jobs/benefits (opens in a new window).

We are a Disability Confident Employer. If you have a disability and meet the essential criteria for this job you will be invited to take part in the next stage of the selection process.

We are a research university with a global reputation for excellence. Our ideas and expertise change the world for the better, making a real difference to society. We know that when people come together with different views, approaches and insights it can lead to richer, more creative and innovative teaching and research and the highest levels of student experience. Our University Vision (www.sheffield.ac.uk/vision) outlines our commitment to building a diverse community of staff and students that recognises and values the abilities, backgrounds, beliefs and ways of living for everyone.