Data-Centric Offshore Site Characterisation for Sustainable and Resilient Infrastructure
Doctoral Training Grant Funding Information
This funding model includes a 36 month fully funded PhD Studentship, set in-line with UK Research & Innovation values. For 2025/6, this will be £20,780 per year. The tax-free stipend will be paid monthly. This PhD Studentship also includes a Full-Time Fee Scholarship for up to 3 years. The funding is subject to your continued registration on the research degree, making satisfactory progression within your PhD, as well as attendance on and successful completion of the Postgraduate Certificate in Research Practice.
All applicants will receive the same stipend irrespective of fee status.
Application Closing Date:
Midday (UK Time) on Wednesday 17 September 2025 for a start date of 2 February 2026.
How to apply
To apply, please follow the below steps:
- Complete the BCU Online Application Form
- Complete the Doctoral Studentship Proposal Form in full, ensuring that you quote the project ID. You will be required to upload your proposal in place of a personal statement on the BCU online application form.
- Upload two references to your online application form (at least one of which must be an academic reference).
- Upload your qualification(s) for entry onto the research degree programme. This will be Bachelor/Master’s certificate(s) and transcript(s).
- International applicants must also provide a valid English language qualification. Please see the list of English language qualifications accepted here. Please check the individual research degree course page for the required scores.
Frequently Asked Questions
To help support you to complete your application, please consult the frequently asked questions below:
Project title: Data-Centric Offshore Site Characterisation for Sustainable and Resilient Infrastructure
Project Lead: Dr Shuyin Feng
Project ID: 17 - 46466625
Project description:
Achieving the UK's goal of net-zero greenhouse gas emissions by 2050 requires major developments in renewable energy, especially offshore wind farms, subsea pipelines, and energy storage systems. However, constructing these offshore projects safely and efficiently faces challenges due to uncertainties in underwater ground conditions. Currently, site investigations rely heavily on invasive techniques, which can damage marine ecosystems, incur high costs, and often fail to identify problems that later cause project delays and increased expenses.
This project addresses these challenges by developing new, data-centric methods for offshore site characterisation, closely aligned with the university’s key research theme of Digital Innovation. It combines advanced data science approaches, including artificial intelligence (AI) and machine learning (ML), with traditional laboratory experiments and physical modelling. Initially, data from the British Geological Survey (BGS) open database and industry collaborators will be used to create accurate and reliable predictive models of subsurface conditions.
The project's main goals include reviewing existing investigation methods, understanding how environmental factors like salinity and temperature affect seabed soil properties, developing, training, and validating AI and ML based models. These models will significantly reduce environmental and financial costs by decreasing the need for invasive seabed tests.
The expected outcomes of the research will include improved site investigation practices, reduced environmental impact on marine life, and more reliable offshore infrastructure, supporting key UN sustainability goals (SDG 9, SDG 13, and SDG 14). Collaboration between academic researchers and industry ensures practical solutions, promoting sustainable offshore energy projects essential for the UK's environmental and energy future.
Anticipated findings and contributions to knowledge:
This research will advance offshore geotechnical engineering by developing a novel, data-centric framework for site characterisation. The approach integrates artificial intelligence (AI) and machine learning (ML) with conventional geotechnical methods to improve the accuracy, efficiency, and sustainability of offshore ground investigations. Aligned with the university’s key research theme of Digital Innovation, the project applies cutting-edge data science to a critical real-world challenge in sustainable infrastructure development.
Key findings will include high-resolution predictive models capable of estimating critical subsurface soil parameters using non-intrusive, multi-source datasets. These models will be trained and validated using real-world data from the British Geological Survey (BGS) and industrial collaborators, ensuring practical applicability and robustness. The outcomes will demonstrate the potential to significantly reduce reliance on invasive seabed testing, minimising environmental disruption to marine ecosystems. This directly supports the objectives of UN Sustainable Development Goal 14 (Life Below Water).
Additionally, the research will produce a comprehensive assessment of current site investigation practices and quantify the influence of key offshore environmental factors, such as salinity, hydrostatic pressure, and temperature gradients, on soil behaviour. This will enhance scientific understanding and inform the calibration of predictive tools.
The project’s contribution to knowledge will also include the development of best practice guidelines for sustainable, optimised offshore soil investigations. These guidelines will support safer and more cost-effective infrastructure design and delivery. By integrating digital tools with traditional methods, the research will contribute to the UK’s transition to net-zero emissions and address broader sustainability goals (SDGs 9 and 13), while fostering academic–industry collaboration in the offshore renewable energy sector.
Person Specification:
Entry Requirements:
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To apply for our Built Environment PhD Research Degree you should have, or expect to be awarded, a Master’s degree in a relevant subject area from a British or overseas university.
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Exceptional candidates without a Master’s degree, but holding a first class or upper second class Bachelor’s degree in a relevant subject area, may be considered.
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We also welcome enquiries from potential PhD researchers with appropriate levels of professional experience.
Essential Criteria:
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Demonstrated understanding of geotechnical engineering principles or data-driven modelling and analysis (e.g., machine learning, statistical modelling).
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Candidates must exhibit strong analytical and problem-solving capabilities.
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Proficiency in at least one programming or data analysis language (e.g., Python, MATLAB, R) with experience in data handling, visualisation, or numerical modelling.
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Excellent written and verbal communication skills, with the ability to clearly convey complex technical information to both specialist and non-specialist audiences.
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A clear interest in offshore infrastructure and/or digital innovation in engineering.
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Candidates must be willing to work across disciplines and engage with academic and industrial collaborators.
Desirable Criteria:
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Prior experience in one or more of the following areas: offshore site investigation, seabed or marine engineering, physical modelling in soil or fluid mechanics, large multi-source datasets (e.g., geological surveys, sensor data), or the use of geospatial tools (e.g., ArcGIS, QGIS).
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Familiarity with standard geotechnical laboratory testing techniques—gained through academic study, research projects, or industrial placements.
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Experience in developing or applying machine learning models, statistical methods, or advanced data visualisation techniques.
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Evidence of independent research, such as a dissertation, project report, publication, or contribution to collaborative R&D.
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Participation in undergraduate or postgraduate research projects relevant to offshore energy or environmental engineering.
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Exposure to offshore environments or marine operations through academic projects, internships, or employment.
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Ability to work effectively in multidisciplinary teams, particularly across engineering, computational, and data science domains.
Overseas applicants:
International applicants must also provide a valid English language qualification, such as International English Language Test System (IELTS) or equivalent with an overall score of 6.5 with no band below 6.0.
Contact:
If you have any questions or need further information, please use the contact details below:
- For enquiries about the funding or project proposal, please contact: shuyin.feng@bcu.ac.uk
- For enquiries about the application process, please contact:research.admissions@bcu.ac.uk