KTP Assoc. - Machine Learning Scientist: Electrochemical Product Authentication

The Department of Mathematics at the University of York is a vibrant community of over 50 academic staff and a flourishing cohort of postdoctoral researchers and visiting scholars. We pride ourselves on a research environment that is both world-leading and deeply collaborative, consistently ranking among the top mathematics departments in the United Kingdom for research impact and excellence.

This Knowledge Transfer Partnership (KTP) is a collaboration between Eluceda Ltd, a high-tech detection technology company based in Burnley, Lancashire and the Department of Mathematics at the University of York. Eluceda develops powerful, practical detection solutions for product and brand authentication, biological and chemical testing. The KTP will develop advanced statistical and machine-learning methods to support Eluceda's technological innovation.

Adulterated spirits and counterfeit medicines cause significant harm each year. Eluceda has developed innovative electrochemical sensing technology, currently deployed worldwide for detecting fraud in alcoholic spirits. The handheld "lab-on-a-chip" device tests liquid samples, comparing electrochemical fingerprints to expected profiles. This KTP Project will extend this proven technology into pharmaceutical verification- particularly in emerging markets, where large analytical equipment is unaffordable.

The KTP Associate will be based at Eluceda in Burnley and lead development of a novel analysis pipeline for processing and classifying sample data gathered by Eluceda's sensing technology, in collaboration with mathematicians from the University of York. There are significant training opportunities for developing leadership and project management skills within the KTP programme.

Qualification through: PhD or Master's degree in Mathematics/Statistics/Computer Science or equivalent.

Knowledge of: statistics and machine learning techniques and their practical implementation (Python, MATLAB and/or R).

Skills, abilities and competencies: strong analytical and problem-solving skills; ability to work with complex real-world datasets; ability to communicate machine learning concepts clearly to non-specialist colleagues; proactive, self-motivated and able to take ownership of a strategically important project; ability to work as part of a team and also to work independently using own initiative.

Experience: of carrying out both independent and collaborative research; developing machine learning models using Python/R/MATLAB; with data visualisation, model validation and statistical analysis.

Personal attributes include: Attention to detail and commitment to high quality; collaborative ethos; ability to organise own workload & prioritise own work in response to deadlines.

Interview date: To be confirmed.

For informal enquiries: please contact Dr Jessica Hargreaves on