RA in Automated Formulation and Computational Design of Antimicrobial Delivery

Job description

Can robots and machine learning help us outsmart antimicrobial resistance?

Join a pioneering international project – Nanoheal, from bench to bedside – developing the next generation of antimicrobial and wound-healing materials. You will combine laboratory automation with computational design to discover advanced delivery systems, taking formulation discovery from slow, manual trial and error to rapid, data-driven optimisation.

Antimicrobial resistance is one of the greatest threats to global health, and discovering new antimicrobials is only half the battle. They also need to reach the site of infection, stay active, and work alongside the body’s own healing mechanisms. This project tackles that challenge by developing biocompatible eutecto-gels: multifunctional, non-dehydrating gels built from deep eutectic solvents, combining antimicrobial agents with healing factors.

You will use automated, programmable high-throughput screening technology to build and test a broad library of candidate gel formulations. You will then pair the experimental data with quantum mechanical calculations, molecular dynamics, and cheminformatic descriptors to train machine-learning models. Ultimately, you will optimise gel performance against targets defined with partners across the wider project.

Join us and be part of a vibrant community at Loughborough University delivering high-impact science.

Here’s more about the role and what we’re looking for:

What you will be doing:

• 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 your results with project needs and present your findings.

What we are looking for:

• 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).
• A background in formulation, soft materials, ionic liquids, deep eutectic solvents or gels is a plus, as is familiarity with rheology or thermal analysis.
• The ability to work independently, think across disciplines, and move comfortably between the in-lab experimentation and computation.
• Strong communication skills and a collaborative approach to working in an international team.

You don’t need to tick every box; we welcome applicants strong in either the experimental or the computational side who are keen to grow into the other.

What we offer

• A leading role in a high-impact, EU-funded international project tackling one of the greatest current global health challenges.
• Access to state-of-the-art automation and computational facilities, and the chance to build a distinctive skillset across both experimental and data-driven research.
• Collaboration with partner groups across the network, with opportunities for joint training and knowledge exchange.
• A strong launchpad for your next step. You will have the opportunity to establish a publishable dataset, gain hands-on experience with automated and machine-learning-driven discovery, make international connections, and be supported in developing fellowship and grant applications or follow-on funding.
• A supportive, interdisciplinary research environment and support for your career development.
• An excellent benefits package.

This is a full-time, fixed-term post for 10 months, with part-time working considered pro-rata. Candidates who have not yet been awarded their PhD will be appointed at Research Assistant level.