曼彻斯特大学（英国）PhD position in Quantum-Enhanced Learning of the Precursors to Extreme Events申请条件要求-申请方
主办方
曼彻斯特大学（英国）
PhD直招介绍
About the Project
This PhD will develop quantum machine-learning methods to forecast extreme environmental hazards such as storms, floods, and wildfires. The project will explore how large environmental datasets can be compressed and learned using tensor networks combined with machine learning architectures on quantum computers. Improving the anticipation of extreme events will support resilient decision-making for emergency response, infrastructure, energy networks, and risk management, helping to protect communities and services.
The project will address four linked questions. First, can the high-dimensional environmental systems be compressed into low-dimensional latent representations via tensor networks that preserve and emphasise the early indicators of extreme events? Second, can quantum time-series forecasting models then learn the resulting dynamics, and detect the emergence of such indicators? Third, how can these indicators be designed to sample the time-series and output the warning to the limited output space of the quantum computer? Fourth, when forecasting rare, high-impact weather events, do quantum machine-learning models offer a real advantage over classical approaches in the presence of hardware noise? Can the noise be mitigated for a near-term benefit, or must we wait for fault-tolerant quantum computing for operationally useful predictions?
This project will be supported by Qronon. Qronon is the first UK company developing quantum- and machine-learning-enabled forecasting tools that can deliver precision warnings of floods, fires and hurricanes days before they strike.
Qronon will provide a key industrial and commercialisation perspective, keeping the project grounded in terms of developing methods that are feasible for near-term deployment for real-world impact.
The i-Risk Doctoral Focal Award
i-Risk PhD research offers a unique opportunity to contribute to the generation of new knowledge in the forefront of informatics. i-Risk cohorts will advance understanding and deliver innovative tools and solutions for multi-hazard systemic risk resilience and sustainability practice. Doctoral Researchers will undertake a structured training programme and partner co-created interdisciplinary research projects.
Our Vision
The vision of i-Risk is to train the next generation of research practitioners and leaders who will be at the forefront of collaborative research and:
Integrate informatics with understanding of evolving risk throughout the environment
Collaborate with a broad range of partners from industry, government agencies, global organisations (e.g., the United Nations) and Non-Government Organisations to ensure research directly informs policy and practice, delivering widespread impact.
Core Research Themes
i-Risk builds on 4 leading UK institution’s long-standing strengths at the vanguard of informatics, multi-hazard risk, and resilience research, with unparalleled facilities and >70 multidisciplinary academic supervisors for subject-specific support, providing students with an exceptional research environment.
i-Risk has four core research themes:
Observations, monitoring and understanding
Deploying nascent technologies and intelligent observation/monitoring/experimental approaches to gather rich data to understand evolving hazards.
Modelling and understanding processes/risk
Developing data analytics approaches/tools to model and understand intertwined natural, social, and engineering systems, enabling analysis and characterisation of multi-hazard systemic risks.
Forecasting, prediction and early warning
Predicting and forecasting hazard risks for timely, reliable warnings, facilitating elective risk mitigation and community/infrastructure resilience.
Risk communication and management solutions
Delivering innovative tools and solutions supporting sustainable multi-hazard systemic risk management, rendering hazard/risk information accessible to/intelligible by end-users/stakeholders, advancing sustainability practice and policy.
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.
Please note that interviews are anticipated to be held remotely via Microsoft Teams week commencing 29 June 2026.
曼彻斯特大学（英国） PhD position in Quantum-Enhanced Learning of the Precursors to Extreme Events项目有没有奖学金，是不是全奖Phd招生，下面我们一起看一下【大学名称】Phd的奖学金资助情况
项目资助情况
Funding Notes
This 3.5-year PhD studentship is funded by the NERC i-Risk DFA and is open to Home (UK) and overseas students. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£21,805 for 2026/27; subject to annual uplift), and tuition fees will be paid. We expect the stipend to increase each year. The start date is October 2026.
We recommend
that you apply early as the advert may be removed before the deadline.
曼彻斯特大学（英国）Phd申请条件和要求都有哪些？PhD position in Quantum-Enhanced Learning of the Precursors to Extreme Events项目是不是全奖？有没有奖学金？下面我们一起看一下曼彻斯特大学（英国）申请Phd直招需要具备哪些条件和要求，以及托福、雅思语言成绩要到多少才能申请。
申请要求
Eligibility
Applicants should have, or expect to achieve, an excellent academic record (UK First-class or 2.1 honours or international equivalent depending on the funding source) in Engineering, Earth Sciences, Computing or another related physical science discipline (MSc, MSci or BSc).
Applicants should have a strong quantitative background in applied mathematics, physics, engineering, or a related discipline. They should be confident in differential equations and Python for mathematical modelling. Familiarity with topics such as machine learning, quantum computing or fluid dynamics would be beneficial.
报名方式
联系人
姓名：admissions team
邮箱：FSE.doctoralacademy.admissions@manchester.ac.uk
招生人信息
Dr P Brearley, Dr TL Howarth, Prof L Magri, Dr O Ahmed
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