动态水下环境中用于位置识别和变化检测的机器人监测博士研究员
Robotic Monitoring for Place Recognition and Change Detection in Dynamic Underwater 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 Engineering & the Environment (7175) • 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
- 2027-10-31
- 全部申请截止
- （Europe/London）
- 资助原文
- Fees and funding Fully funded for UK, EU and International students. Tuition fees will be paid and you'll receive a tax-free living stipend.
- 资助条件
- 未确认申请人可获得资助，请以学校审核结果为准。
- 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
- 该项目开发新型机器人建图和AI技术，以检测和记录大型动态海底环境中微妙的环境变化与水下基础设施。通过结合先进的定位、高分辨率传感和概率数据分析，在具有挑战性的无GNSS水下环境中实现对环境变化和水下基础设施的长期可靠监测。工作将使用仿真、水箱中的控制实验以及通过AUV收集的数据进行验证，并且是欧盟资助的BlueOcean玛丽·居里博士网络（Marie Skłodowska-Curie Doctoral Network）的一部分，包含两次为期三个月的短期借调（secondments）。
- 申请条件
- 申请者需持有英国2:1荣誉学位或其国际同等学历；在招募之日不得拥有博士学位；可以是任何国籍；在招募日期前36个月内，在英国居住或主要活动（工作、学习等）的时间不得超过12个月。
- 待遇
- 全额资助，涵盖英国、欧盟和国际学生，学费全免并获得免税生活补助（tax-free living stipend）。
- 申请方式
- 申请人需通过南安普顿大学系统在线申请，选择项目类型为Research、2027/28学年、工程与物理科学学院（Faculty of Engineering and Physical Sciences），选择全职或兼职，搜索项目代码PhD Engineering & the Environment (7175)，并在申请的第2节中添加导师姓名。截止日期为2027年10月31日。
- 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
- 2027-10-31
- 学科
- 工程
- 合同类型
- 项目资助
- 本站收录
- 内容更新
- 导师
- Blair Thornton
- 来源
- 南安普顿大学博士研究项目招生 · 最近核对 2026-10-10
- 详情核验
判定依据（原文摘录）
- bachelor_ok为false
A UK 2:1 honours degree or its international equivalent.
- english_ok为true
You'll be part of the EU-funded BlueOcean Marie Skłodowska-Curie Doctoral Network, working alongside 13 PhD researchers across leading European institutions.
原文
View all current projects
Postgraduate research project
Robotic Monitoring for Place Recognition and Change Detection in Dynamic Underwater Environments
Funding
Fully funded (UK and international)
Type of degree
Doctor of Philosophy
Entry requirements
2:1 honours degree
2:1 honours degree
View full entry requirements
Faculty graduate school
Faculty of Engineering and Physical Sciences
Closing date
31 Oct 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
This project develops novel robotic mapping and AI techniques to detect and document subtle changes in dynamic seafloor environments over large spatial scales. By combining advanced localisation, high-resolution sensing and probabilistic data analysis, the project will enable reliable long-term monitoring of environmental change and subsea infrastructure in challenging GNSS-denied underwater environments.
Understanding how the seafloor evolves is critical for environmental monitoring and subsea infrastructure inspection. However, the natural processes and human activities that drive changes are gradual, meaning that between seasonal or yearly deployments, the magnitude of change at any point on the seafloor remains below to the detection limit of even high-resolution mapping sensors. In addition, aligning scenes across repeat surveys is difficult due to navigation drift, evolving scene structure, and sensitivity to environmental conditions.
This project aims to address this by combining accurate robotic localisation and high-resolutions laser-based seafloor mapping with advanced machine learning feature detection to enable evolving subsea scenes to be robustly aligned across surveys. You'll develop probabilistic models to detect subtle changes under noise and uncertainty. Your work will be validated using simulation, controlled experiments in tank facilities, and both existing and new datasets gathered using an AUV.
You'll be part of the EU-funded BlueOcean Marie Skłodowska-Curie Doctoral Network, working alongside 13 PhD researchers across leading European institutions. The programme provides interdisciplinary training in robotics, sensing, and AI for marine monitoring, as well as access to internationally recognised experts and industry partners. You will also undertake two three-month secondments with organisations including CNR (Italy) and Voyis Imaging (Canada), and work closely with PhD students hosted at the NTNU (Norway) to develop an integrated system for high-resolution, large-scale ocean observation combining advanced sensors, AI-driven analysis, and autonomous underwater vehicles.
The School of Engineering 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
Professor Blair Thornton
Professor of Marine Autonomy
Research interests
• seafloor 3D visual reconstruction: development of deep-sea imaging hardware and processing pipelines for calibration, localisation and 3D mapping of the seafloor with full-field uncertainty characterisation
• automated interpretation of data: development of AI methods for rapid scalable interpretation of seafloor imagery
• robotics: development of low-cost, long endurance seafloor imaging floats and highly intelligent and manoeuvrable robotic imaging platform for visual survey of complex environments
Supervisors
Dr Xiaohao Cai
Lecturer in Computer Science
Research interests
• Image/signal/data processing
• Computer vision
• Machine learning
Entry requirements
A UK 2:1 honours degree or its international equivalent.
This project is funded via the European Commission, and as such recruitment is taking place following the European Code of Conduct for Recruitment of Researchers, which all candidates are encouraged to study. You:
• must not have a doctoral degree at the date of their recruitment
• can be of any nationality
• must not have resided or carried out their main activity (work, studies, etc.) in the UK for more than 12 months in the 36 months immediately before their recruitment date
Fees and funding
Fully funded for UK, EU and International students. Tuition fees will be paid and you'll receive a tax-free living stipend.
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 Engineering & the Environment (7175)
• 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
For questions about applying, email our Doctoral College (doctoralcollege-admissions@soton.ac.uk).
Project leader
For an initial conversation, email Professor Blair Thornton (B.Thornton@soton.ac.uk).