R Curry

This PhD project at The University of Manchester, within the Department of Materials, aims to revolutionize atomic-scale characterisation of quantum materials. Leveraging state-of-the-art transmission electron microscopy (TEM) and advanced deep/machine learning (DL/ML) techniques, the research will develop automatic, quantitative, and statistically representative imaging methods for local structure, composition, and bonding at atomic resolution across millimeter-scale areas in semiconductor systems. Quantum technologies require precise doping of isotopically selected single impurity ions into materials such as silicon, SiGe, diamond, and 2D materials. Current optical methods can locate single atom qubits to 1μm spatial resolution but cannot probe their atomic environment. TEM is uniquely capable of characterising these buried defect sites, but traditional approaches are manual and time-consuming. This project will overcome these limitations by developing automated, high-speed, event-responsive scanning and DL feature identification, enabling the automatic location and imaging of point defects in semiconductor crystals at the atomic scale. Electron energy loss spectroscopy will further allow full characterisation and manipulation of the local bonding environment. The new characterisation capability will unlock the ability to reliably produce large arrays of isotopically selected ions, forming the basis for fully error-corrected quantum computers. The successful candidate will gain expertise in advanced TEM, DL/ML, and materials science, preparing for a career as an independent researcher in quantum technologies. Funding is available for tuition fees, a UKRI minimum annual stipend (£20,780), and up to £5,000 per year for research training support, covering the full 4-year duration. Flexible study arrangements, including part-time options, may be considered depending on project and funding requirements. Applicants must have an upper second-class (2:1) honours degree in a relevant discipline or a 2:1 plus a Master’s degree at merit. Application requires a personal statement, CV, details of previous study, and two referees. Equality, diversity, and inclusion are central to the university’s ethos, and applicants from diverse backgrounds and career paths are encouraged. Applications are accepted year-round, but early submission is recommended as the advert may be removed before the deadline. Contacting the supervisors prior to application is strongly advised. For more information and to apply, visit the project page.

This PhD project at The University of Manchester, titled 'New Frontiers for Atomic Imaging of Quantum Materials', aims to revolutionize the atomic-scale characterization of quantum materials using advanced transmission electron microscopy (TEM) combined with deep/machine learning (DL/ML) and human vision approaches. The research will focus on developing automatic, quantitative, and statistically representative imaging methods to analyze local structure, composition, and bonding with atomic resolution across millimeter-scale areas in semiconductor systems. One of the central challenges addressed is the deterministic doping of isotopically selected single impurity ions into materials such as isotopically enriched silicon, SiGe, diamond, and 2D materials. This is crucial for the advancement of spin and photonic-based quantum technologies. Current optical methods can locate single atom qubits in semiconductors to a spatial resolution of 1μm but cannot probe their local atomic environment. TEM is the only technique capable of characterizing these buried defect sites, but it is currently manual and labor-intensive, requiring millions of atomic images to find specific features. This limitation has hindered the understanding and optimization of qubit defects. The project will overcome these challenges by developing advanced TEM imaging approaches powered by DL/ML control. Automation, high-speed event-responsive scanning, and DL feature identification will enable the automatic location and imaging of point defects in semiconductor crystals at the atomic scale. Once defect features are identified, electron energy loss spectroscopy will be used for full characterization and manipulation of the local bonding environment. This world-first capability will unlock the reliable production of large arrays of isotopically selected ions, forming the basis for fully error-corrected quantum computers. The successful candidate will gain expertise in materials for quantum technologies, TEM instrumentation, DL/ML methods, and semiconductor physics, preparing for a future as an independent researcher. The project is supervised by Professors S Haigh and R Curry in the Department of Materials. Funding includes full tuition fees, a UKRI minimum annual stipend (£20,780), and a research training support grant of up to £5,000 per year for four years. Applicants should hold an upper second-class (2:1) honours degree in a relevant discipline or a 2:1 honours degree plus a Master’s at merit in a relevant field. The University of Manchester values diversity and inclusion, encouraging applications from all backgrounds and offering flexible study arrangements. Applications are accepted year-round, but early submission is recommended as the advert may be removed before the deadline. To apply, complete the formal application via the online portal, specifying the project name, supervisor(s), previous study details, and two referees. A personal statement and CV are required. Contacting the supervisors before applying is strongly recommended.

This PhD project at The University of Manchester, titled 'New Frontiers for Atomic Imaging of Quantum Materials', aims to revolutionize the atomic-scale characterisation of quantum materials using advanced transmission electron microscopy (TEM) combined with deep/machine learning (DL/ML) and human vision approaches. The research will focus on developing automatic, quantitative, and statistically representative imaging methods to resolve local structure, composition, and bonding at atomic resolution across millimeter-scale areas in semiconductor systems. One of the key challenges in quantum technologies is the deterministic doping of isotopically selected single impurity ions into materials such as isotopically enriched silicon, SiGe, diamond, and 2D materials. While optical methods can locate implanted single atom qubits in semiconductors to a spatial resolution of 1μm, they cannot probe the local atomic environment. TEM is the only technique capable of characterising these buried defect sites, but it is currently manual and labor-intensive. Locating a specific atomic feature within a 1μm square field of view could require up to 10 million atomic TEM images, making the process impractical and leaving many qubit defects uncharacterised. This project will overcome these limitations by developing advanced TEM imaging approaches powered by DL/ML control. Automation, high-speed event-responsive scanning, and DL feature identification will enable the automatic location and imaging of point defects in semiconductor crystals at the atomic scale. Once defect features are identified, electron energy loss spectroscopy will be used for full characterisation and manipulation of the local bonding environment. The resulting world-first characterisation capability will unlock the ability to reliably produce large arrays (~1 million) of isotopically selected ions, forming the basis for a fully error-corrected quantum computer. The successful candidate will gain expertise in TEM, DL/ML, materials characterisation, and quantum technologies, preparing for a future career as an independent researcher in materials for quantum technologies. The project is supervised by Professors S Haigh and R Curry in the Department of Materials. The University of Manchester is committed to equality, diversity, and inclusion, actively encouraging applicants from diverse backgrounds and offering flexible study arrangements, including part-time options. Funding for this project covers tuition fees, a UKRI minimum annual stipend (£20,780 per annum), and up to £5,000 per annum research training support grant for the full 4-year programme. Applicants should have an upper second-class (2:1) honours degree in a relevant discipline or a 2:1 honours degree plus a Master’s degree at merit in a relevant field. Applications are accepted year-round, but early application is recommended as the advert may be removed before the deadline. To apply, candidates must complete a formal application through the online portal, specifying the project name, supervisor(s), previous study details, and contact information for two referees. A personal statement and CV are required. Prospective applicants are strongly encouraged to contact the supervisors before applying.