AI Based Tools for Subsea Cable Routing for Offshore Wind Farms (Ref: CO/VG-SF1/2026)

About the Project

Cable routing is a non-trivial aspect of offshore wind farm development. An optimal route must balance multiple factors such as thermal efficiency, soil type, benthic communities, shipping traffic, fishing zones, etc. Inadequate consideration of these factors can lead to severe financial and environmental consequences. Financially, cable failures are projected to cost €61.5 billion globally over the next decade (TGS Energy Data & Intelligence 2024, tinyurl.com/55sh23v9). Environmentally, subsea cables can disrupt habitats and benthic ecosystems, and their electromagnetic fields (EMF) have been shown to affect certain marine species.

With the UK’s strategic expansion of offshore wind, route planning needs to be robust, environmentally responsible and transparent (e.g., provide detailed explanations for agencies such as The Crown Estate). However, the current cable route planning relies on a combination of GIS tools, surveying, optimization and past experience. While effective for small projects, these methods can become increasingly limited as offshore wind farms continue to grow larger, go further into the sea and generate more power (tinyurl.com/4dmxvmkd). Planners must synthesize vast, multidisciplinary knowledge across geotechnical engineering, marine ecology, and maritime traffic which may result in cognitive overload and subjective biases. Additionally, route planners may lack awareness of newly emerging ecological findings (e.g., EMF sensitivity of some local species).

The overall goal of this PhD is to develop novel AI based systems which can help the planners to develop efficient (cost wise), neighborhood friendly (e.g., less disruption to shipping traffic, avoiding marine conservation zones, etc.), thermally efficient (e.g. accounting for the impact of burial depth and soil composition on the thermal performance of the cable) and easy to deploy paths (e.g., the underlying sea bed is conducive to cable installation to the appropriate depth).

The student will join the Language and Data research group (https://ldr-lboro.github.io/) at the Loughborough University. The student will also get a chance to interact with scientists at AURA CDT (The EPSRC Centre for Doctoral Training in Offshore Wind Energy Sustainability and Resilience, auracdt.hull.ac.uk/cdt2/). AURA is a multi-institution, transdisciplinary center for doctoral training set up by the EPSRC, UKRI. This consortium consists of leading scientists and academics (in the area of offshore wind energy) from across four different UK HEI (including Loughborough University) and dozens of relevant industry partners. The overall goal of the CDT is to train and mentor the future leaders of offshore wind energy in the UK.

The primary supervisor, Dr Gunturi is a Lecturer in the department of computer science. He has over 10 years of experience in GeoSpatial Data Systems and Data Science. He has worked extensively in various scalability and semantic aspects of answering routing queries on geo-spatial datasets. Prof Dethlefs is a Professor of Computer Science (Artificial Intelligence) at Loughborough University. She heads the new Language and Data research group, and leads recruitment and talent pipeline activities for two EPSRC/NERC funded CDTs in Offshore Wind Energy. Over the years, she has made significant contributions to developing Explainable AI for Operations & Maintenance of wind turbines. Examples of which include forecasting turbine faults from SCADA and alarm data; decision support systems via conversational human-AI collaboration, and physics-based machine learning for structural health monitoring.

Name of primary supervisor/CDT lead: Dr. Viswanath Gunturi https://www.lboro.ac.uk/departments/compsci/staff/venkata-maruti-viswanath-gunturi/

Name of secondary supervisor: Prof Nina Dethlefs https://www.lboro.ac.uk/departments/compsci/staff/nina-dethlefs/

Entry requirements: Bachelors (Preferably Masters) level degree in computer science (or Artificial Intelligence) and passion to work in offshore wind power sector.

English language requirements: Applicants must meet the minimum English language requirements. Further details are available on the International website (http://www.lboro.ac.uk/international/applicants/english/).

Bench fees required: No

Closing date of advert: 31st July 2026

Start date: October 2026

Full-time/part-time availability: Full-time 3 years, Part-time 6 years

Fee band: 2025/26 Band RB (UK £5,006, International £28,600)

How to apply: All applications should be made online. Under programme name, select Computer Science. Please quote the advertised reference number: CO/VG-SF1/2026 in your application. To avoid delays in processing your application, please ensure that you submit a CV and the minimum supporting documents (https://www.lboro.ac.uk/study/postgraduate/apply/research-applications/how-start-research-application/). The following selection criteria (https://www.lboro.ac.uk/study/postgraduate/apply/research-applications/studentship-assessment-criteria/) will be used by academic schools to help them make a decision on your application. Please note that this criteria is used for both funded and self-funded projects. Please note, applications for this project are considered on an ongoing basis once submitted and the project may be withdrawn prior to the application deadline, if a suitable candidate is chosen for the project.

Project search terms: artificial intelligence, offshore wind Farms

Email Address Sci: sci-pgr@lboro.ac.uk