Complex chemical mixtures are everywhere: medicines, foods, catalysts, biological fluids, and the products of chemical reactions. They matter because the exact make-up of a mixture often determines whether something works, is safe, or is stable. A powerful way to determine what is in a mixture is nuclear magnetic resonance (NMR) spectroscopy – but there is a catch. In real samples, many signals pile on top of one another, making spectra difficult to interpret.
This PhD is about making NMR far more useful for messy, real-world samples. The aim is to develop new ways to clean up NMR spectra so that we can identify, and measure, individual components even when their signals are strongly overlapped. If successful, the methods would make mixture analysis faster, more reliable, and more quantitative, with clear benefits for applications ranging from pharmaceuticals and reaction monitoring to food science and metabolomics.
The work will combine hands-on experiments with smart data analysis. On the experimental side, you will design and test new NMR measurement methods that pull out different kinds of information about molecules (for example how fast they move, how they interact, or how they are connected). The key idea is that if you can collect several independent pieces of information about the same sample, you can disentangle signals that look identical in a conventional spectrum. On the data analysis side, you will build well-defined workflows and code to turn these richer datasets into clear chemical answers.
The project has three connected themes:
1. New NMR experiments – developing and implementing pulse sequences that improve spectral clarity and make results more trustworthy for quantitative work.
2. NMR that works in realistic conditions – adapting methods so they remain robust for practical problems such as reaction monitoring and stability studies, where samples are imperfect and time matters.
3. Data-driven mixture analysis – developing open-source processing tools and exploring whether modern statistical or machine-learning approaches genuinely improve signal separation, assignment, and classification.
You will join a world-leading group in NMR methods development, with a strong track record of sharing new experiments and software with the wider community, and with active collaborations across academia, industry, and instrument manufacturers. The application focus can be tailored to your interests (for example pharmaceuticals, catalysis, porous materials, food, or biological mixtures), while keeping a clear core of method development.
This is a four-year PhD for self-funded students, with applications accepted year-round. We are looking for students with a strong chemistry (or related) background who enjoy careful problem-solving and are curious about combining lab-based measurement with computational analysis. Prior experience with NMR and/or programming (Python, MATLAB, or similar) is welcome but not required – training and support will be provided
Eligibility
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.
Funding
This is a self funded project. At Manchester we offer a range of scholarships, studentships and awards at university, faculty and department level, to support both UK and overseas postgraduate researchers applying for competition and self-funded projects.
For more information, visit our funding page or search our funding database for specific scholarships, studentships and awards you may be eligible for.
Before you apply
We strongly recommend that you contact the supervisor(s) for this project before you apply. 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.
How to apply
Apply online through our website: https://uom.link/pgr-apply-2425
When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.
Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.
After you have applied you will be asked to upload the following supporting documents:
If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk.
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.
We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.
We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).