PhD Position: Construction, Characterisation and Analysis of Results from a Liquid Fingerprinting System Using Ultrasound Kingston University in United Kingdom

This PhD project at Kingston University focuses on the construction, characterisation, and analysis of a liquid fingerprinting system using ultrasound. The system aims to identify liquids in situ from small samples with high reliability, addressing needs in process industries (such as drinks, fuels, and perfumes), environmental water analysis, and biological fluids (including sweat, blood, urine, tears, and cerebrospinal fluid). Initial interests include milk, soft drinks, alcoholic drinks, and olive oils.

The research involves further development and refinement of an existing ultrasound-based system that characterises liquids by their mechanical and rheological properties as pendant drops. The project will require the physical construction of a working prototype, demanding proficiency in electronics and strong mechanical skills. Previous work has demonstrated the system's ability to distinguish between similar alcoholic drinks, dilutions of red wine, and specific aviation biofuels.

A significant aspect of the project is the analysis of a large dataset of recorded signals, as well as new measurements generated during the research. This analysis will employ deep learning pattern recognition techniques, such as Convolutional Neural Networks (CNNs), building on methods previously used for classifying acoustic signals from knee joints. The ideal candidate should have expertise in applied physics, electronic engineering, or chemical analysis, and be proficient in deep learning methods, preferably within the Matlab environment. Essential qualities include commitment, enthusiasm, teamwork, and self-motivation for independent work.

The position is part of the Graduate School studentships competition for October 2026 entry, with funding potentially covering tuition and stipend. For more information on funding and application procedures, candidates should consult the Kingston University PhD Studentships page and the Faculty of Engineering, Computing and the Environment research page. The application deadline is March 4, 2026.

References supporting the research include studies on wavelet, Fourier, and PCA data analysis pipelines for liquid mixtures, ultrasonic tensiographic measurements for liquid fingerprinting, and deep learning classification of acoustic emission data. This project offers a unique opportunity to contribute to interdisciplinary research at the intersection of physics, engineering, and artificial intelligence, with broad applications in industry and healthcare.