曼彻斯特大学（英国）PhD position in SmartPipe: AI-Enabled Digital Twins and Distributed Fibre Optic Sensing for Multi-Hazard Risk Management of Water Networks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
UK water utilities face a dual challenge: aging infrastructure and a regulatory mandate to slash leakage by 17% by 2030. This project, SmartPipe, develops a "digital nervous system" for buried water networks using Distributed Fibre Optic Sensing and AI-enabled Digital Twins. By detecting "silent" failures - like slow leaks or soil erosion - before they become catastrophic bursts, we can prevent supply disruptions like the 2025 Tunbridge Wells crisis. Working with industry leaders, the researcher will create tools that ensure water networks are not only resilient to climate change but are managed equitably to protect all communities.
Leveraging Distributed Fibre Optic Sensing (DFOS), this project transforms pipelines into "smart assets" capable of real-time strain and temperature sensing. By integrating this "big data" with Artificial Intelligence and Digital Twins, we can move beyond reactive repairs. Furthermore, following frameworks for equitable infrastructure planning, this project will explore how digital oversight can prioritize resilience in vulnerable or underserved service areas, ensuring that the £104 billion investment cycle delivers social equity alongside technical reliability.
Research questions
How can DFOS signatures be interpreted and decoupled to distinguish between ambient operational noise and early-stage pipeline distress (e.g. leakage-induced soil erosion or joint deformation)?
How can physics-informed machine learning integrate soil–pipeline interaction and hydro-mechanical processes to quantify pipeline condition and Remaining Useful Life (RUL) under multi-hazard loading?
How can a Digital Twin framework fuse real-time sensing data with environmental and operational data to enable scalable, automated early-warning systems for water networks?
The project will be supported by industry partners including WSP and Mott MacDonald, both of whom have extensive experience in water infrastructure, asset management, and digital engineering.
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.
曼彻斯特大学（英国） PhD position in SmartPipe: AI-Enabled Digital Twins and Distributed Fibre Optic Sensing for Multi-Hazard Risk Management of Water Networks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 SmartPipe: AI-Enabled Digital Twins and Distributed Fibre Optic Sensing for Multi-Hazard Risk Management of Water Networks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).
The project is inherently interdisciplinary, spanning geotechnical engineering, sensing technologies, and data science. While the i-Risk programme will provide strong training in digital informatics and transferable skills, the following prior competencies would be highly beneficial:
Strong quantitative background: A solid foundation in mathematics, mechanics, or engineering science is essential to engage with soil-structure interaction modelling and physics-informed machine learning approaches.
Programming skills: Prior experience in Python (or similar) is required for data processing, machine learning implementation, and integration within digital twin frameworks.
Fundamentals of data analysis / machine learning: Familiarity with basic statistical analysis and machine learning concepts (e.g. regression, classification, time-series analysis) will enable the student to effectively develop and interpret AI models.
Basic understanding of engineering systems: Background knowledge in civil, environmental, or mechanical engineering, particularly in infrastructure systems or geotechnics, would be advantageous for understanding pipeline behaviour and failure mechanisms.
Willingness to work across disciplines: The project requires the ability to integrate experimental data, physical modelling, and computational methods, and to engage with both academic and industry stakeholders.
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 X Xu, Prof J Harou
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