About the Project
A 3.5-year PhD studentship in NMR spectroscopy and data analysis is available at the University of Manchester, supervised jointly by Prof. Mathias Nilsson and Dr Ralph Adams.
Diffusion NMR spectroscopy measures how quickly molecules move through a liquid sample, and is one of the most widely used techniques for studying polymers, mixtures, and everything from drug formulations to battery electrolytes. Yet almost every diffusion NMR experiment run in the last forty years has thrown away most of the information it recorded. This studentship sets out to change that.
When a sample contains molecules of many different sizes, as every real polymer does, a diffusion NMR measurement carries information about the full distribution of sizes present, not just the average. The standard analysis reads out the average and stops. There are existing attempts to do better, based on numerical methods that try to reconstruct the full distribution of sizes from the noisy data, but this is a mathematically fragile calculation and the answers cannot be trusted. Our new approach avoids the problem: it extracts the statistical properties of the distribution (its average, its spread, and its shape) directly, without reconstructing the distribution itself. In computer simulation, existing reconstruction methods return distribution-width estimates that are wrong by hundreds of percent; the new approach is accurate to fractions of a percent.
The purpose of the studentship is to move this from simulation into the laboratory, using well-characterised commercial polymer standards where the answer is already known. Success would establish a new way of analysing NMR data with applications in the design of more sustainable materials, more effective drugs, and better batteries, and in any system where molecules of different kinds behave differently.
Day to day, you will design and run NMR experiments on the group's spectrometers, write and use scientific Python to process and simulate data, and work closely with your supervisors and the wider group. You will have the opportunity to contribute to other projects in the group and to present your work at international conferences.
The balance between experimental NMR, simulation, and data analysis can be shaped around your interests and strengths. No prior experience of NMR is required, and full training will be provided in every aspect of the project.
The NMR Methodology group at Manchester is one of the leading groups of its kind in the world, with excellent NMR facilities and substantial in-house computing, strong links to the major NMR instrument manufacturers, and active collaborations with academic and industrial groups worldwide. Recent PhD graduates from the group have gone on to research positions at instrument manufacturers, in industry, and in universities across the world.
The group has a strong track record of publication with PhD students as first authors, in leading journals including JACS, Angewandte Chemie, and Analytical Chemistry. Recent PhD students have typically published several papers during their studentship, and you will be supported and encouraged to do the same.
You will develop transferable skills in scientific Python and statistical analysis, experimental design and instrument control, presentation of research at international conferences, and scientific writing.
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 Natural Sciences 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 your 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 (or international equivalent) in Chemistry, Physics, Mathematics or Data Science OR any upper-second class (2:1) honours degree and a Master's degree at merit (or international equivalent) in Chemistry, Physics, Mathematics or Data Science. Applicants are preferred to have a background in spectroscopy, programming, and laboratory work.
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_SoNS
Funding Notes
Funding covers tuition fees, an annual stipend at the UKRI minimum rate (currently £21,805 per year), and a research training support grant of up to £2,500 per year for the full 3.5-year programme.