RA: Formulation & ML-Driven Antimicrobial Delivery

Loughborough University; Sharnford; On-site; GBP 30,000 - 36,000; Full time

Benefits offered by this job

EU-funded project involvement

State-of-the-art automation facilities

International collaboration

Job summary

Loughborough University invites applications for a fixed-term, 10‑month post in the Nanoheal project, combining lab automation with computational design to advance antimicrobial and wound‑healing materials. You will work from bench to data‑driven optimization.

You will perform high‑throughput screening, integrate experimental and computational data, and apply machine learning to optimise gel formulations, collaborating with international partners around the world.

Qualifications

• A PhD (or close to completion) in chemistry, chemical engineering, materials science, soft matter physics, computational chemistry, or a related discipline.
• Hands-on experience with laboratory automation or high-throughput experimentation, and/or strong skills in computational modelling or machine learning (Python experience is highly desirable).
• Background in formulation, soft materials, ionic liquids, deep eutectic solvents or gels is a plus, as is familiarity with rheology or thermal analysis.
• Ability to work independently, think across disciplines, and move between in-lab experimentation and computation.
• Strong communication skills and a collaborative approach to working in an international team.

Responsibilities

• Adapting formulation methods for on-platform gelation and rapid functional assessment using automated screening.
• Preparing and characterising gels, including rheology, viscometry and thermal analysis, to build a high-quality experimental property database.
• Integrating experimental and computational data to build predictive models and identify promising new formulations.
• Using machine learning to optimise gels across a range of functional parameters.
• Working with partners across the globe to align results with project needs and present findings.

Skills

Lab automation

Computational modelling

Machine learning

Python (desirable)

Education

PhD near completion

Chemistry, chemical engineering, materials science, soft matter physics, computational chemistry or related

Tools

Automation platforms

High-throughput screening

Job description

Loughborough University invites applications for a fixed-term, 10‑month post in the Nanoheal project, combining lab automation with computational design to advance antimicrobial and wound‑healing materials. You will work from bench to data‑driven optimization.

You will perform high‑throughput screening, integrate experimental and computational data, and apply machine learning to optimise gel formulations, collaborating with international partners around the world.