动态水下环境中用于地点识别与变化检测的机器人监测技术

该项目致力于开发机器人测绘与人工智能技术，以在具有挑战性的水下环境中监测海底变化。通过整合先进的定位技术、高分辨率传感技术与概率数据分析方法，旨在实现长期环境监测及海底基础设施检查。申请者应具备机器人技术、传感技术及人工智能方面的技能。

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.

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

资助：完全由资金支持（英国及国际资金） 学费与资助 我们为英国本土学生和国际学生提供多种资助机会。符合条件的学生可通过“地平线欧洲”学费减免，自动免除国际学生学费与英国本土学生学费之间的差额。 大学通过竞争性遴选授予的校长助学金，为遴选排名靠前的申请人支付国际学生学费与英国本土学生学费之间的差额。 本校各学院通过竞争性遴选提供的博士奖助金，通常为遴选排名靠前的申请人支付按英国本土学生标准收取的学费，并提供生活津贴。 资助采用滚动授予方式，因此请尽早申请，以尽可能增加进入资助评选的机会。 如需了解更多信息，请访问我们的研究生研究资助页面。

如何申请 您需： 选择课程类型（研究型）、2027/28学年、工程与物理科学学院 选择全日制或非全日制 搜索工程与环境博士项目（7175） 在申请表第2部分填写导师姓名 申请材料应包括： 您的CV（个人简历） 2份学术推荐信 截至目前的学位课程成绩单和证书 英语语言资格证明（如适用）