面向跨环境大规模分布式光纤传感的机器学习博士研究员
Machine learning for large-scale distributed acoustic sensing across environments
原帖优先：申请材料、截止时间与资格以原帖和学校官方说明为准。
博士招生与资助公告
- 申请年度
- 2027；原帖说明：How to apply Apply now You need to: • choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences • select Full time or Part time • search for programme PhD Computer Science (7089) • add name of the supervisor in section 2 of the application Applications should include: • your CV (resumé) • 2 academic references • degree transcripts and certificates to date • English language qualification (if applicable)
- 最近申请截止
- 2027-07-01
- 全部申请截止
- （Europe/London）
- 资助原文
- Fees and funding Due to funding limits, only UK and EU-based candidates are invited to apply. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students. Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants. Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants. Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered. For more information, please visit our postgraduate research funding pages.
- 资助条件
- 原帖为竞争性或附条件资助说明，未确认本项目获资助及申请人能获奖；不作为保证全奖。
- 原帖材料说明
- • your CV (resumé)
- • 2 academic references
- • degree transcripts and certificates to date
- • English language qualification (if applicable)
确定性信息来自对应版本的完整原帖。原帖只提供日期，日末和学校当地时区为本站转换假设。筛选使用最后申请截止；资助与录取以学校审核结果为准。
AI 中文速览
- 研究内容
- 本项目利用光纤电缆作为城市、海岸和海洋的大规模传感器，开发针对最先进分布式光纤传感（DAS）数据集的机器学习和信号处理方法，分析复杂的时空信号并应用于基础设施、交通、环境监测和海洋传感。
- 申请条件
- 申请人必须持有英国2:1荣誉学士学位或计算机科学、电子与电气工程、数据科学、物理学、数学、声学或其他相关学科的国际同等学历；具备使用Python或MATLAB的编程和定量分析能力以及机器学习技能。
- 待遇
- 竞争性学生奖学金通常涵盖英国本土水平的学费以及生活费津贴；提供高性能计算设施访问权限以及参与学术会议和教学经验的机会。由于资助限制，仅限英国和欧盟申请者。
- 申请方式
- 通过南安普顿大学申请系统提交申请，选择研究型项目（Research）、2027/28学年、工程与物理科学学院、全职或兼职模式，搜索PhD Computer Science (7089)项目，并在申请第二部分添加导师姓名。
- 材料清单
- 简历 (CV)
- 2封学术推荐信
- 迄今为止的学位成绩单和证书
- 英语语言资格证明（如适用）
由 gemini-2.5-flash-lite 生成，博士岗判定置信度 100%。
岗位信息
- 最终轮次截止
- 2027-07-01
- 学科
- 计算机科学
- 合同类型
- 项目资助
- 本站收录
- 内容更新
- 原帖导师候选（待核验）
- Rafael Mestre, Mohammad Belal
- 来源
- 南安普顿大学博士研究项目招生 · 最近核对 2026-10-24
- 详情核验
判定依据（原文摘录）
- is_phd
Type of degree Doctor of Philosophy
- english_ok
English language qualification (if applicable)
- bachelor_ok
You must have a UK 2:1 honours degree, or its international equivalent
原文
View all current projects
Postgraduate research project
Machine learning for large-scale distributed acoustic sensing across environments
Funding
Competition funded
Competition funded
View fees and funding
Type of degree
Doctor of Philosophy
Entry requirements
2:1 UK honours degree
2:1 UK honours degree
View full entry requirements
Faculty graduate school
Faculty of Engineering and Physical Sciences
Closing date
1 Jul 2027
On this page
About the project
Potential supervisors
Entry requirements
Fees and funding
How to apply
Contact us
About the project
Potential supervisors
Entry requirements
Fees and funding
How to apply
Contact us
About the project
Use fibre-optic cables as large-scale sensors across cities, coasts, and oceans. This PhD will develop machine-learning and signal-processing methods for state-of-the-art distributed acoustic sensing datasets. You will analyse complex spatiotemporal signals, combine them with environmental and operational data, and investigate applications in infrastructure, transport, environmental monitoring, and marine sensing.
Distributed Acoustic Sensing (DAS) turns optical fibres into a dense array of vibration sensors. It could transform how we monitor infrastructure, transport, environmental processes, human activity, and marine environments. However, DAS produces large, noisy datasets that are difficult to label, creating challenges for signal processing and machine learning.
This PhD will develop computational methods for analysing DAS data across urban and maritime settings. You will have access to state-of-the-art datasets collected across the south of England and London, alongside maritime datasets. DAS measurements can be combined with secondary datasets, including vehicle counts, air-pollution records, and local-authority information to contextualise and validate the signals detected by the fibre. These datasets create opportunities for data fusion, cross-environment comparison, and rigorous validation.
You will shape the technical contribution. Possible directions include self-supervised or weakly supervised learning; event detection, classification, localisation, and source separation; representation learning for spatiotemporal signals; uncertainty and interpretability; domain adaptation across cables, locations, and environments; efficient high-volume processing; and validation using incomplete ground truth. Applications could include transport, infrastructure, environmental hazards, ocean acoustics, marine activity, or ecological monitoring.
The project builds on capabilities and datasets developed through SOUNDSCALE project. You will work across computer science, engineering, signal processing, environmental research, and responsible innovation, with access to high-performance computing and collaboration with the National Oceanography Centre (NOC) and researchers working across urban and marine sensing applications.
You will join the University of Southampton’s School of Electronics and Computer Science, ranked 12th in the UK for Computer Science and 4th for Electrical and Electronic Engineering in the Complete University Guide 2027. The ECS Graduate School provides training in research methodology and transferable skills, supplemented by professional-development opportunities through the Doctoral College.
You will have access to Iridis, the University’s high-performance computing facility, and opportunities to work with researchers and facilities at the National Oceanography Centre.
Further opportunities may include teaching experience, conference participation, engagement with the Alan Turing Institute and Responsible AI UK, and support from Future Worlds to explore the commercial potential of your research.
The School of Electronics & Computer Science is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
Potential supervisors
Lead supervisor
Dr Rafael Mestre
PhD, MSc, BSc
Lecturer
Research interests
• Emerging technologies
• Multimodal AI
• Applied and responsible AI
Supervisors
Professor Mohammad Belal
Entry requirements
You must have a UK 2:1 honours degree, or its international equivalent in one of the following:
• computer science
• electrical or electronic engineering
• data science
• physics
• mathematics
• acoustics
• or another relevant discipline
Essential skills:
• programming and quantitative analysis, especially in Python or MATLAB
• machine learning
Desirable skills (applicants are not expected to have all of these skills):
• experience with deep learning
• digital signal processing
• time-series analysis
• high-performance computing
• large scientific datasets
Prior knowledge of distributed acoustic sensing or fibre-optic sensing is not required.
Due to funding limits, only UK and EU-based candidates are invited to apply.
Fees and funding
Due to funding limits, only UK and EU-based candidates are invited to apply.
Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.
Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.
Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.
Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.
For more information, please visit our postgraduate research funding pages.
How to apply
Apply now
You need to:
• choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences
• select Full time or Part time
• search for programme PhD Computer Science (7089)
• add name of the supervisor in section 2 of the application
Applications should include:
• your CV (resumé)
• 2 academic references
• degree transcripts and certificates to date
• English language qualification (if applicable)
Contact us
Faculty of Engineering and Physical Sciences
If you have a general question, email our Doctoral College (doctoralcollege-admissions@soton.ac.uk).
Project leader
For an initial conversation, email Dr Rafael Mestre (r.mestre@soton.ac.uk).