The success and widespread use of the next generation of quantum technology will fundamentally depend on the materials used to make them. In particular, the way these materials interact with light drives many quantum technology applications, from quantum computing to sensing to communications. As these technologies move towards commercialisation, it is vital that the most relevant properties are preserved as the materials leave the laboratory and enter the real world, where a multitude of external factors will have an influence. One of these is the presence of crystalline defects, including extended defects such as dislocations; another is the influence of interfaces, as these defects are often fabricated close to the surface.

Disentangling and controlling the individual influence of these factors on the relevant light-matter interactions experimentally can be difficult. A compelling alternative is therefore to simulate the interaction of light with these materials from first principles (i.e., directly from the equations of quantum mechanics). In simulations, we can control these external factors much more easily, getting a better understanding of the physics in play. For these complex systems, however, gaining this computational insight can require simulating several thousand atoms to high levels of accuracy, which is challenging with existing computational methods.

In this PhD project, we will make use of cutting edge computational methods, including linear-scaling time-dependent density functional theory (LS-TDDFT), quantum embedding, and machine learning potentials, to drive a step-change in the understanding of these environmental effects. The project will focus on a particular class of materials for quantum technology – that of colour centres in crystalline semiconductors, the most well-known being the nitrogen-vacancy centre in diamond – and consider the influence of extended defects and surface effects on the excited state properties of the system. The project will aim to explore several different contenders for quantum technology applications, providing insight into the fundamental physics of these systems, their robustness against these environmental influences, and how best to fabricate them. An important aspect of this work will be comparing results and predictions against experimental data where available; there will also be the opportunity to collaborate with world-leading experimental groups in this area.

If the student is interested, there will also be scope within the project for developing more powerful computational modelling methods for describing complex systems at a quantum mechanical level, based on quantum embedding. This development work would be done alongside other members of the group.

This project will suit a student with an interest in computational modelling and/or materials for quantum technology, and a background in physics, chemistry, materials science, or related disciplines. An interest in code development, especially for materials modelling software, would be beneficial, but not necessary.

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

Funding

At Manchester we offer a range of scholarships, studentships and awards at university, faculty and department level, to support both UK and overseas postgraduate researchers applying for competition and self-funded projects.

For more information, visit our funding page or search our funding database for specific scholarships, studentships and awards you may be eligible for.

Please discuss funding options with the supervisor.

Before you apply

We strongly recommend that you contact the supervisor(s) for this project before you apply. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.

How to apply

Apply online through our website: https://uom.link/pgr-apply-2425

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language certificate (if applicable)

If you have any questions about making an application, please contact our admissions team by emailing [email protected] .

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).