This PhD project, based at the University of Southampton within the EPSRC Centre for Doctoral Training in Quantum Technology Engineering, focuses on quantum optimal control for long-lived NMR methods in methylene-rich systems. The research aims to extend the lifetime of entangled nuclear spin states by developing a new theoretical model to predict and optimize experimental lifetimes, leveraging quantum optimal control techniques. The project investigates CH2 and CF2 moieties, common in organic molecules and amino acids, which contain spin-½ pairs capable of forming entangled states. Long-Lived States (LLS) and Long-Lived Coherences (LLC) are central to the work, offering lifetimes significantly longer than individual spin-½ nuclei and enabling the creation of complex correlated states, sensitive detection of spin environments, and quantum computing applications using nuclear spins as qubits.

The research will involve comprehensive modeling of molecular systems, relaxation pathways, and dynamics, with numerical calculations in Liouville space. The outcome will be a theoretical and experimental 'toolbox' for efficient excitation and improved 'spin memory' of entangled states, with applications in hyperpolarization and high-field NMR, including studies on alpha-synuclein, an intrinsically disordered protein. International collaboration will provide access to specialized samples and facilities.

The position is ideal for candidates with strong theoretical backgrounds and coding skills. The program offers substantial training in scientific, technical, and commercial skills, and is supported by a competitive UKRI TechExpert stipend of approximately £31k per year for UK students, with funding options for EU, Horizon Europe, and international applicants. The University of Southampton is committed to equality, diversity, and sustainability, and welcomes applications from candidates seeking part-time study.

Applicants must hold at least a UK 2:1 honours degree or international equivalent and submit a CV, two academic references, degree transcripts/certificates, and proof of English language proficiency if applicable. The application deadline is 31 July 2026, with an earlier deadline of 31 March 2026 for international applicants.