[PhD by Enterprise FSE] Infrared quantum cascade laser microscopy for invasive bladder cancer IQ-Scan

About the Project

Are you an ambitious, multidisciplinary researcher motivated to translate cutting-edge science into real-world clinical impact? This unique PhD by Enterprise project offers the opportunity to develop a novel diagnostic technology at the interface of physics, data science, and medicine, while gaining the skills to launch a future healthcare venture.

This project focuses on developing and validating a clinically translatable diagnostic platform based on infrared (IR) hyperspectral imaging for the assessment and risk stratification of bladder cancer biopsy tissue. IR hyperspectral imaging is an advanced, label-free optical technique that captures a full biochemical spectrum at every pixel of a tissue sample, generating detailed molecular maps of proteins, lipids, and nucleic acids. Unlike traditional histopathology, which relies on subjective visual interpretation, this approach provides quantitative, reproducible biochemical data, opening the door to more objective and precise diagnostics.

The scientific foundation for this work is strong. Early studies demonstrated that IR imaging could classify bladder tissue, but progress was historically limited by slow imaging speeds and immature analytical methods. These barriers have now been overcome. Recent work from leading researchers in Manchester has shown that IR spectroscopy can accurately identify cancer and predict outcomes in prostate disease. With advances in both imaging technology and machine learning, the field is now primed for translation into bladder cancer, where unmet clinical need remains high.

As a PhD candidate, you will work at the forefront of this emerging field. The project is structured in three phases. First, you will receive training in IR imaging, spectral data processing, and machine learning, while contributing to a comprehensive literature review. In parallel, you will help interrogate a unique bladder cancer tissue microarray using clinically annotated samples from an established biobank.

In the second phase, you will generate high-dimensional imaging datasets using state-of-the-art IR hyperspectral systems. You will then apply advanced machine learning approaches, including patch-based convolutional neural networks, to classify tissue types and uncover biochemical signatures associated with disease.

In the final phase, you will develop an IR-derived risk score, a novel predictive tool designed to stratify patients based on disease aggressiveness and clinical outcome. This risk score represents a clear pathway to intellectual property and commercialisation, forming the foundation for a potential diagnostic product.

This is not a traditional PhD. As part of the PhD by Enterprise Programme, you will receive dedicated training in entrepreneurship, innovation, and venture development. You will be supported to explore market opportunities, develop a business plan, and potentially contribute to the creation of a spin-out company based on your research.

We are seeking highly motivated candidates with backgrounds in physics, engineering, data science, or related disciplines, and a strong interest in applying quantitative approaches to healthcare challenges. Experience in programming or machine learning is advantageous but not essential.

This project offers a rare opportunity to combine world-class research with real-world impact, developing a technology that could fundamentally change how cancer is diagnosed—while building the foundations of a future venture.

Applicants are expected to hold (or be on track to obtain) a minimum upper second-class undergraduate honours degree (or equivalent) in a relevant discipline such as physics, biomedical engineering, data science, computer science, chemistry, or a related quantitative field. A strong academic background in analytical or computational methods is essential. Experience in one or more of the following areas is desirable: machine learning, image analysis, signal processing, spectroscopy, or biomedical/clinical data analysis. Prior research experience (e.g. final year project, internship, or publication) demonstrating the ability to work independently and handle complex datasets will be advantageous.

Given the interdisciplinary nature of the project, we are particularly interested in candidates who are comfortable working across traditional subject boundaries and are motivated to apply quantitative techniques to real-world clinical challenges. Programming experience (e.g. Python, MATLAB or similar) and familiarity with data-driven approaches are highly desirable, though training will be provided.

This PhD is part of an Enterprise programme, and as such we are seeking candidates with curiosity, initiative, and an interest in innovation and translation. Applicants should be motivated not only by scientific discovery but also by the opportunity to develop impactful technologies with potential for commercialisation. Strong communication skills, a collaborative mindset, and a willingness to engage with clinicians, researchers, and industry partners will be essential for success.

To apply for this project please select PhD Enterpise (FSE).

FSE_Enterprise

Funding Notes

Fully funded studentships are available to start in 2026/27 and provide the following:

• Funding at the UKRI stipend rate, £21,805
• Tuition fees
• Up to £20,000 RTSG per studentship, depending on the research project
• Additional support for entrepreneurship training and customer discovery activities
• Visa and immigration costs reimbursed for successful international PGRs