Research Assistant: AI-Enabled Inspection and Digital Twin for Critical Infrastructure (Offshore)

Location: City Centre Campus (Millennium Point)

Fixed Term contract for 24 months

We are seeking a Research Assistant: AI-Enabled Inspection and Digital Twin for Critical Infrastructure (Offshore) to join our Department of Architecture and the Built Environment, School of Architecture, Built Environment, Computing and Engineering (ABCE), contributing to internationally funded research developing AI-driven inspection and digital twin systems for offshore and coastal maritime infrastructure.

In this role, you will play a central role in BCU’s contribution to the MariSens project – a European project developing autonomous, AI-enabled inspection and digital twin systems for offshore and coastal maritime assets, including offshore wind structures, harbour infrastructure, subsea pipelines, and port facilities. Key responsibilities include multi-modal AI algorithm development for underwater and surface inspection, digital twin integration for maritime asset management, and collaborative R&D with a large European consortium, working closely with consortium partners spanning research institutions, SMEs, and large industrial end-users including offshore energy operators, harbour authorities, and maritime service providers to ensure services are efficient, compliant, and aligned to University priorities.

Key Responsibilities

• Develop AI and machine learning algorithms for automated defect detection, anomaly classification, and condition assessment of offshore and coastal infrastructure using UAV-collected inspection data
• Design and validate digital twin models for maritime assets, integrating AI-derived UAV inspection outputs for predictive maintenance and asset management applications
• Process and analyse aerial inspection datasets from offshore and coastal environments, developing robust algorithms capable of operating under challenging maritime conditions
• Collaborate with consortium partners on the integration of AI analytics and digital twin outputs into operational inspection workflows for offshore energy, harbour, and coastal infrastructure end-users
• Produce peer-reviewed journal papers, conference presentations, technical reports, and dissemination materials aligned with MariSens project obligations
• Engage with end-users and maritime industry stakeholders to ensure AI inspection and digital twin outputs are operationally relevant and aligned with offshore asset maintenance workflows

Essential Requirements

• A minimum 2:1 undergraduate degree in Computer Science, Artificial Intelligence, Software Engineering, Civil/Offshore Engineering, Ocean Engineering, Digital Built Environment, or a closely related discipline
• Experience with AI and machine learning techniques for sensor data analysis, computer vision, or detection and classification tasks in complex environments
• Proficiency in Python and/or other relevant programming languages for data processing, model development, and system integration
• Strong understanding of data management for large, multi-modal datasets from inspection, monitoring, or remote sensing systems
• Excellent written and verbal communication skills, with the ability to present technical findings to both academic and industry audiences
• Ability to manage workload independently, meet project milestones, and work effectively within a large international multi-partner research consortium

Desirable Requirements

• MSc or PhD in Artificial Intelligence, Data Science, Ocean/Offshore Engineering, Digital Built Environment, Marine Technology, or a closely related discipline
• Familiarity with underwater sensing technologies, sonar systems, or remote inspection methods for subsea or offshore structures
• Knowledge of digital twin platforms, asset lifecycle management, or predictive maintenance frameworks, particularly in maritime or offshore contexts
• Understanding of autonomous unmanned vehicle systems (UAV, USV, UUV) and their application to inspection and monitoring tasks
• Awareness of the offshore energy sector, harbour/port operations, or marine environmental monitoring and associated data challenges
• Experience in a collaborative or industry-facing research environment such as a funded research project, KTP, or industrial placement

For an informal discussion about the role, please contact Dr Saeed Talebi at Saeed.Talebi@bcu.ac.uk.

Closing Date: 23.59 hours BST on Thursday 30 April 2026

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