AI/ML Scientist – Predictive Modelling & Optimisation of Sustainable Polymers

A&B Smart Materials is an Oxford University-based materials science venture founded by Oxbridge graduates. We are pioneering sustainable, biobased, and biodegradable superabsorbent polymers (SAPs) to replace synthetic, petroleum-based SAPs. These polymers - used in hygiene products and agriculture - represent a $9B global market today, projected to grow to $17.6B by 2035. With the EU banning synthetic SAPs in soils by 2028, A&B Smart Materials is positioned to lead this transition. We have already secured traction with global leaders.

We are seeking an AI/ML Scientist to apply machine learning and data science techniques to accelerate the discovery and optimisation of sustainable polymers. You will work closely with chemists and engineers to design predictive models that guide experiments, reduce trial-and-error, and identify the most promising formulations for scale-up.

This is a pioneering role where AI meets deeptech R&D, with the potential to cut years off the materials development cycle.

Responsibilities

• Build predictive ML models to estimate polymer properties (e.g. absorption, swelling, biodegradability) from formulation and synthesis data.
• Develop algorithms to optimise formulations for performance and cost simultaneously.
• Use experimental datasets to train, validate, and refine models, ensuring practical relevance.
• Collaborate with chemists to design experiments guided by model predictions.
• Explore generative AI approaches to propose novel biodegradable SAP formulations.
• Integrate computational workflows with lab processes to create a closed-loop discovery cycle.
• Present insights to both technical and commercial teams to inform R&D and strategy.

Essential qualifications and skills

• MSc/PhD in Computer Science, Data Science, Computational Chemistry, Materials Informatics, or a related field.
• Strong programming skills in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Experience applying ML/AI to materials science, chemistry, or process engineering datasets.
• Ability to collaborate across disciplines and communicate results to non-technical stakeholders.
• Curiosity and drive to apply AI to real-world sustainability challenges.

Desirable

• Background in polymer chemistry, hydrogels, or biobased materials.
• Experience with Bayesian optimisation, generative models, or active learning for materials discovery.
• Familiarity with cheminformatics or molecular dynamics modelling.
• Track record of publications or patents in applied ML for science.

Location: Oxford (Begbroke Science Park). Hybrid/remote flexibility possible.

Start date: ASAP.

Impact: You will create the digital engine that drives the next generation of sustainable polymers, ensuring our lab discoveries reach global markets faster and more efficiently.

Please note that while the EIT budget is currently exhausted, funding is still available under the EIC, ERC, MSCA, and RI pathways.