PhD Studentship: Use of machine learning for high-fidelity mass spectrometry data stitching

Research theme: Chemometrics

The project is jointly funded by the Community of Analytical Measurement Sciences and the University of Manchester Department of Chemistry. The successful candidate will receive an annual tax free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be paid. This funding is for home students.

Analytical platforms like gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and ion mobility spectrometry (IMS) generate distinct yet complementary datasets. Integrating these datasets has the potential to create holistic views of complex chemical and biological systems. However, discrepancies in data structure, measurement scales, and resolution across platforms make this integration challenging. Existing approaches often fail to preserve the fidelity of combined datasets, leading to loss of crucial information.

This proposal aligns directly with the CAMS Data Analytics Theme and the Grand Challenge of using machine learning (ML) for high-fidelity data ‘stitching’. The integration of data from multiple analytical platforms is critical for advancing the understanding of complex biological and chemical systems. This work specifically addresses the identified challenge by leveraging ML to overcome barriers associated with platform heterogeneity, including differences in resolution, scale, and feature representation. By developing advanced algorithms that align, merge, and aggregate datasets while maintaining data fidelity, the project contributes to the CAMS goal of enabling precise, accurate, and actionable analytical insights. The outcomes of this research will support cross-disciplinary applications such as environmental monitoring, chemical analysis, and systems biology, providing innovative solutions that enhance the integration and utilisation of diverse datasets. The proposed methodologies also expand the utility of data generated within the CAMS community, driving the development of standardised practices and tools for data integration in the broader analytical sciences.

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

To apply, please contact Dr Drupad Trivedi - drupad.trivedi@manchester.ac.uk. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.

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