Research Assistant in Automated Formulation and Computational Design
Drive innovative antimicrobial delivery research combining laboratory automation, soft materials characterization, and machine learning at Loughborough University.
Best for:Best for researchers with a background in chemistry, materials science, or computational modeling keen on combining laboratory automation and machine learning.
Overview
Loughborough University seeks a dedicated Research Assistant to contribute to an EU-funded international project focused on automated formulation and computational design for antimicrobial delivery. The successful candidate will prepare and characterize gels, implement automated screening methods, integrate experimental data with computational models, and apply machine learning techniques to optimize material performance. This full-time, fixed-term position offers an exceptional opportunity to work within an interdisciplinary team bridging experimental science and data-driven discovery. Applicants with a strong background in either the experimental or computational domain are encouraged to apply and expand their interdisciplinary expertise.
What you'll do
- Adapt formulation methods for on-platform gelation and rapid functional assessment using automated screening systems.
- Prepare and characterize gels using rheology, viscometry, and thermal analysis to construct an extensive property database.
- Integrate experimental results with computational models to identify and design promising new formulations.
- Apply machine learning algorithms to optimize gel characteristics across various functional parameters.
- Collaborate with international project partners to align research outcomes with objectives and present findings.
Requirements
Degree required: PhD (or close to completion) in chemistry, chemical engineering, materials science, soft matter physics, computational chemistry, or a related discipline
Language: English; strong professional proficiency
Instruction: English
- Hands-on experience with laboratory automation or high-throughput experimentation, or strong computational modeling and machine learning skills
- Ability to conduct in-lab physical characterization such as rheology, viscometry, or thermal analysis
- Proficiency or strong interest in Python programming for data analysis and machine learning
- Capacity to work independently and bridge experimental and computational workflows
- Strong communication skills and collaborative aptitude for multidisciplinary international teamwork
- Candidates who have not yet been awarded their PhD will be appointed at Research Assistant level
Funding & terms
How to apply
Deadline: 2026-11-22
Interested candidates should submit their application through the university's online recruitment portal before the specified deadline. Ensure all required documentation detailing your academic and practical experience is properly uploaded.
- CV
- Cover letter
Contact
Anna Croft· Professor
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