PhD Studentship: Marie Sklodowska-Curie Actions Doctoral Network BlueOcean

Full-Time Fixed-Term for 36 months

This PhD aims to develop novel robotic monitoring and data processing methods to document changes in dynamic seafloor environments over large spatial scales. 

Motivation: Understanding how the seafloor evolves is critical for environmental monitoring and subsea infrastructure inspection. Natural processes and human activities drive changes over time, yet detecting these reliably remains challenging. Improved methods could enable early identification of risks to ecosystems and infrastructure, while providing quantitative evidence to support effective intervention and adaptive management.

Challenges: Documenting change in GNSS-denied subsea environments presents significant challenges. While changes occur over large spatial scales, change is often 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, recognising and aligning scenes across repeated surveys is difficult due to navigation drift, evolving scene structure, and sensitivity to environmental conditions.

Research questions:

• How can evolving seafloor scenes be consistently recognised and spatially aligned across repeated subsea surveys in GNSS-denied environments?
• How can small seafloor changes be reliably detected when they are comparable to sensor noise?
• How can full-field uncertainty be modelled and propagated to support large-scale change analysis?

Approach: You will combine advanced robotic localisation and high-resolution mapping methods with advanced machine learning feature detection to enable evolving subsea scenes to be robustly aligned across repeat surveys. You will develop probabilistic models to detect subtle changes under noise and uncertainty. Your work will be validated using simulation, existing datasets from the University of Southampton’s Smarty200 AUV, and new data collected during field campaigns with the Ocean Perception group. Controlled experiments will also be conducted in the Maritime Robotics and Instrumentation Laboratory and its dedicated 8x8x6m deep-water tank.

Training and Environment: You will 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 up to two three-month secondments with organisations including CNR (Italy) and Voyis Imaging (Canada).

Eligibility:

This PhD is funded via the European Commission. Recruitment is taking place following the

European Code of Conduct for Recruitment of Researchers.

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.

The candidate:

• 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.

To apply, please provide: 

• Curriculum Vitae
• Two reference letters
• Degree Transcripts/Certificates to date

If you are applying for this role, you must also apply for the PhD below.

https://www.phdscanner.com/opportunities/phd-vacancies-university-of-southampton-united-kingdom-robotic-monitoring-for-place-recognition-and-change-detection-in-dynamic-underwater-environments-c1cb18e8-c472-46e8-9a50-54c02cf5de44

Further details: Job Description and Person Specification

Advert information