About the Project
Recent advances in chemical imaging are transforming our ability to investigate the molecular composition of complex biological systems. Infrared spectroscopic imaging and mass spectrometry imaging can provide highly complementary information about tissue structure and molecular chemistry, from the tissue level down to cellular and sub-cellular scales. However, combining these different types of data remains a major scientific and computational challenge.
This PhD project will develop new experimental and artificial intelligence (AI) approaches for integrating multimodal chemical imaging data. The student will work with state-of-the-art imaging technologies including Quantum Cascade Laser (QCL) infrared imaging, Optical Photothermal Infrared (OPTIR) imaging, Desorption Electrospray Ionisation Mass Spectrometry Imaging (DESI-MSI) and Secondary Ion Mass Spectrometry (SIMS).
The overarching aim is to develop new data fusion and machine learning methodologies capable of combining complementary chemical and molecular information from these different imaging platforms into unified representations of complex samples.
The project will combine experimental imaging with computational method development. The student will establish correlative workflows that enable the same or related tissue samples to be investigated using multiple imaging techniques. A key challenge will be registering and aligning images acquired at different spatial resolutions and using fundamentally different measurement technologies.
Particular emphasis will be placed on developing deep learning approaches that exploit spectral and spatial information simultaneously. The project will investigate multimodal data fusion and representation learning, including approaches that can identify relationships between information obtained using different imaging modalities. An exciting longer-term objective will be to explore cross-modal prediction: determining whether molecular information measured using one imaging technique can be predicted from data acquired using another.
Disease-associated tissues, including cancer samples, will provide challenging biological systems in which to develop and validate these approaches. However, the emphasis of the PhD is on developing broadly applicable imaging and computational methodologies rather than disease-specific biomarker discovery. The resulting approaches could ultimately have applications across biomedical imaging, materials characterisation, environmental analysis and chemical sensing.
The successful candidate will join a highly interdisciplinary research environment spanning spectroscopy, mass spectrometry, imaging science and AI. They will receive training in advanced chemical imaging, experimental design, image analysis, machine learning, deep learning and scientific programming, with access to state-of-the-art QCL, OPTIR, DESI-MSI and SIMS instrumentation and high-performance computing facilities.
This project would particularly suit a candidate interested in working at the interface between experimental science and data science. It offers an opportunity to develop a distinctive combination of advanced analytical, imaging and computational skills that are increasingly important in both academic research and industry.
This project is expected to start in September 2027.
Before you apply: We strongly recommend that you contact the supervisors for this project before you apply.
How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships. If you don't already have an applicant account, please follow the instructions here.
When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications. Please note late or incomplete applications will not be considered.
Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status.
We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder.
Eligibility: The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD Chemistry, Analytical Chemistry, Data Science, Computer Science (or international equivalent) OR any upper-second class (2:1) honours degree and a Master's degree at merit in a discipline directly relevant to the PhD Chemistry, Analytical Chemistry, Data Science, Computer Science (or international equivalent). Previous experience in biomedical research, the interface of chemical imaging, data science and artificial intelligence is desirable.
This project will remain open until filled.
If your application is submitted by 1st November 2026, you can expect a decision by 18th December 2026.
If your application is submitted by 15th January 2027, you can expect a decision by 30th March 2027.
Self or externally funded students can also be considered for this project.
FSE_SoE
Funding Notes
Funding for this project covers tuition fees, UKRI minimum annual stipend (currently £21,805/annum) and up to a £2.5k/annum research training support grant for the full duration of the 3.5-year programme.