Bayesian NMR Explorer: Tracing Chemical Origins

Nuclear Inst; Southampton; On-site; GBP 18,000 - 21,000; Full time

Benefits offered by this job

Funding opportunities

Job summary

The University of Southampton is offering a PhD project to determine the origin of chemical compounds from spectroscopic fingerprints. You will combine NMR data with Bayesian inference and machine learning to decode these signatures for forensic science and chemical security.

Collaboration with Dstl and the Interdisciplinary Centre for Magnetic Resonance will help you develop probabilistic models that handle measurement uncertainty and spectral correlations, with training in Python and

Qualifications

• UK 2:1 honours degree or international equivalent in chemistry, physics, engineering, mathematics, statistics, or closely related disciplines.
• Essential skills: interest in applying statistical and computational techniques to challenging problems in chemical analysis; willingness to work across disciplinary boundaries.
• Beneficial skills: experience in Python programming; statistical modelling; NMR spectroscopy.

Responsibilities

• Develop probabilistic models for NMR data using Bayesian methods.
• Investigate isotopic spectral signatures to attribute origins.
• Collaborate with Dstl and the Interdisciplinary Centre for Magnetic Resonance at Southampton.
• Implement scalable analyses in Python and manage datasets.

Education

UK 2:1 honours degree or international equivalent in chemistry / physics / engineering / mathematics / statistics

interdisciplinary collaboration

statistical modelling

Python programming

data analysis

Tools

Python

Job description

The University of Southampton is offering a PhD project to determine the origin of chemical compounds from spectroscopic fingerprints. You will combine NMR data with Bayesian inference and machine learning to decode these signatures for forensic science and chemical security.

Collaboration with Dstl and the Interdisciplinary Centre for Magnetic Resonance will help you develop probabilistic models that handle measurement uncertainty and spectral correlations, with training in Python and