[School of Natural Sciences PhD Scholarships] First Physics and AI-enhanced scientific discovery with the DarkSide-20k direct dark matter experiment

About the project

About the Project This PhD project will give you the opportunity to carry out some of the first physics exploitation of the DarkSide-20k dark matter observatory, a next-generation international experiment being constructed 1.4 km beneath a mountain at the Gran Sasso National Laboratory in Central Italy. DarkSide-20k will use around 50 tonnes of ultra-pure liquid argon as a target to search for the extremely rare interactions expected if particle-like dark matter passes through the detector. Following commissioning in 2027, it is expected to become the world’s most sensitive experiment of its kind, with an operational lifetime of around a decade. The University of Manchester and the wider UK community play major roles in DarkSide-20k. Our group leads activity in advanced photosensor instrumentation, detector simulation, AI-enabled analysis, and the development of high-fidelity digital twins of the experiment. These tools are essential for modelling possible dark matter signals, understanding backgrounds that can mimic them, and optimising the detector and analysis to maximise its discovery reach. As the PhD student, you will: - Contribute to the installation and commissioning of Manchester-built radiopure silicon photosensor arrays in the DarkSide-20k neutron veto system, use recorded in situ performance to improve detector simulations, and explore the potential of the highly instrumented veto system for additional physics measurements beyond its primary background-rejection role. - Develop generative-AI digital twins trained on calibration and commissioning data to replace computationally costly detector simulations, enabling fast, high-fidelity modelling of signal and background processes and improved statistical inference. - Develop and deploy AI-enabled real-time techniques to identify and classify low-level pulses in silicon photosensors, using unsupervised-learning and pulse-shape-discrimination techniques to separate possible dark matter interactions from noise and detector backgrounds. - Analyse the first physics data from DarkSide-20k for a range of non-standard dark matter candidate models, using these advances to extend the experiment’s discovery potential and scientific reach. You will receive training in: - Silicon photosensor operation and low-background instrumentation. - Astroparticle physics and rare-event searches. - Computational detector simulation and digital-twin development. - Generative AI, unsupervised learning, and advanced data analysis. - Statistical inference, high-performance computing, and scientific software. You will join one of the UK’s largest particle physics groups and become a member of both DarkSide-UK, spanning 11 institutes, and the international Global Argon Dark Matter Collaboration of around 500 scientists and engineers. You will work closely with researchers, engineers, and other PhD students in Manchester and internationally, with opportunities to contribute directly to experimental operations and collaboration activities. Subject to additional research funding, we anticipate the possibility of an extended placement at the underground experimental site in Italy. This project will place you at the interface of experimental particle physics, AI, advanced instrumentation, and scientific computing, with the opportunity to contribute directly to one of the leading dark matter searches of the coming decade. The combination of detector, AI, simulation, and large-scale data-analysis expertise will provide strong preparation for careers in fundamental research, data-intensive science, AI, instrumentation, and wider technology sectors. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Natural Sciences Scholarships . If you don’t already have an applicant account, please follow the instructions here . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing your motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in Physics (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in Physics (or international equivalent). Previous research experience in one or more of particle/astroparticle physics, silicon photosensor development, or artificial intelligence workflows for science, is desirable. This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoNS

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