利用NMR指纹进行贝叶斯推断解码化学来源博士研究员

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Postgraduate research project

Decoding chemical origins: Bayesian inference from NMR fingerprints

Funding

Fully funded (UK only)

Type of degree

Doctor of Philosophy

Entry requirements

2:1 honours degree

2:1 honours degree

View full entry requirements

Faculty graduate school

Faculty of Engineering and Physical Sciences

Closing date

31 Aug 2027

On this page

About the project

Potential supervisors

Entry requirements

Fees and funding

How to apply

Contact us

About the project

Potential supervisors

Entry requirements

Fees and funding

How to apply

Contact us

About the project

Can we determine where a chemical came from using its spectroscopic fingerprint? Even chemically identical compounds can exhibit subtle spectral differences arising from variations in isotopic composition, providing clues about their origins. In this project, you will combine Nuclear Magnetic Resonance data with advanced Bayesian methods and machine learning to decode these signatures, for applications in forensic science and chemical security.

Determining the origin of chemical compounds is an important challenge in analytical chemistry, with applications in forensic science, chemical security and environmental monitoring. Although chemically identical compounds share the same molecular structure, subtle variations in isotopic composition can reveal clues about their production history. Nuclear Magnetic Resonance (NMR) spectroscopy can detect these differences, but extracting reliable information from complex, noisy data remains challenging.

In collaboration with the Defence Science and Technology Laboratory (Dstl), this project will develop statistical methods that combine physical understanding of NMR spectroscopy with Bayesian inference. Beyond classifying samples, we aim to identify which spectral features distinguish chemical origins, understand how they vary between samples, and quantify the confidence with which unknown compounds can be attributed to particular sources.

You will develop probabilistic models that account for measurement uncertainty, sample-to-sample variability and correlations between spectral features. Using experimental NMR datasets, you will investigate how subtle isotopic signatures can support reliable source attribution. The project will also explore how machine learning could complement Bayesian methods, particularly by making complex analyses more efficient and scalable.

Working at the intersection of chemistry, statistics and data science, you will gain hands-on experience in advanced data analysis, computational modelling and Python programming. You will have opportunities to collaborate with researchers at Dstl and the Interdisciplinary Centre for Magnetic Resonance at the University of Southampton. Training in statistical methods, programming and NMR interpretation will be tailored to your background, providing valuable skills for research and industry.

Training and guidance will be provided in Python programming, statistical modelling and data analysis, depending on your existing experience and background.

You will also receive support in understanding the fundamentals of NMR spectroscopy and interpreting experimental spectra, so prior expertise in these areas is not required.

The School of Chemistry & Chemical Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.

Potential supervisors

Lead supervisor

CB

Dr Christian Bengs

Lecturer in Magnetic Resonance

Research interests

• Dynamic Nuclear Polarization in Nitrogen-Vacancy Center

• Hydrodynamics in Disordered Spin Systems

• Quantum Computation Methods for NMR

Entry requirements

You must have a UK 2:1 honours degree or its international equivalent in one of the following:

• chemistry

• physics

• engineering

• mathematics

• statistics

• or closely related disciplines

Essential skills:

• an interest in applying statistical and computational techniques to challenging problems in chemical analysis

• motivation to work across disciplinary boundaries

Beneficial but not essential skills:

• experience in Python programming

• statistical modelling

• NMR spectroscopy

Fees and funding

We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.

Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.

Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.

Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

For more information, please visit our postgraduate research funding pages.

How to apply

Apply now

You need to:

• choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences

• select Full time or Part time

• choose the relevant PhD Chemistry (7189)

• add name of the supervisor in section 2 of the application

Applications should include:

• your CV (resumé)

• 2 academic references

• degree transcripts to date

• English language qualification (if applicable)

Contact us

Faculty of Engineering and Physical Sciences

If you have a general question, email our Doctoral College (doctoralcollege-admissions@soton.ac.uk).

Project leader

For an initial conversation, email Dr. Christian Bengs (c.bengs@soton.ac.uk).