Location: City Centre Campus (Millennium Point)
Fixed Term contract for 36 months
We are seeking a Research Assistant: AI-Enabled Digital Twin for Critical Infrastructure Inspection to join our Department of Architecture and the Built Environment, School of Architecture, Built Environment, Computing and Engineering (ABCE), contributing to internationally funded research at the forefront of AI-driven infrastructure inspection and digital twin development.
In this role, you will drive BCU’s research contributions across two European projects: STRUCTURE, which develops AI-enabled UAV inspection and digital twin systems for transportation infrastructure with a focus on bridges; and MariSens, which develops equivalent capabilities for offshore and coastal maritime assets including offshore wind structures, harbour infrastructure, and subsea assets.
You will develop AI-enabled inspection algorithms and digital twin frameworks spanning both domains. Key responsibilities include AI model development, sensor data fusion, digital twin integration, and consortium-level collaboration, working closely with international academic and industrial partners across the UK and Europe to ensure services are efficient, compliant, and aligned to University priorities.
Key Responsibilities
Develop and implement AI and machine learning algorithms for automated defect detection and condition assessment of critical infrastructure using UAV-collected inspection data
Develop digital twin frameworks that integrate AI-derived inspection outputs for both transportation infrastructure, with a focus on bridges, and offshore/coastal maritime assets
Coordinate and work with research associates across the STRUCTURE and MariSens projects, providing cross-programme technical leadership and ensuring alignment between UAV inspection and digital twin research activities in bridge and maritime inspection domains
Collaborate with consortium partners across the UK and Europe on platform integration, system validation, and the translation of research outputs into operational inspection workflows
Produce peer-reviewed journal papers, conference presentations, technical reports, and other dissemination outputs
Engage with infrastructure operators, regulatory bodies, and maintenance service providers to ensure research outputs are aligned with real-world operational needs
Essential Requirements
A minimum 2:1 undergraduate degree in Computer Science, Artificial Intelligence, Software Engineering, Civil Engineering, Digital Built Environment, or a closely related discipline
Demonstrable experience with AI and machine learning frameworks for computer vision, sensor data analysis, or structural/geospatial data processing
Proficiency in Python and/or other relevant programming languages for data processing, model development, and system integration
Strong understanding of data management, including structured and unstructured datasets from sensor or inspection systems
Excellent written and verbal communication skills, with the ability to present complex technical findings to both academic and industry audiences
Ability to manage workload independently, meet project milestones, and collaborate effectively within a large international research consortium
Desirable Requirements
MSc or PhD in Artificial Intelligence, Data Science, Digital Built Environment, Infrastructure Engineering, or a closely related discipline
Experience with digital twin platforms, Building Information Modelling (BIM), or asset lifecycle management systems
Familiarity with UAV/drone inspection systems or autonomous aerial data collection for infrastructure applications
Knowledge of civil engineering, transportation infrastructure, or offshore/marine structures and their inspection and maintenance requirements
Understanding of IoT platforms, connected sensor networks, or real-time data streaming systems relevant to infrastructure monitoring
Experience in a collaborative or industry-facing research environment such as a funded research project, KTP, or industrial placement
A track record of academic publication or research dissemination
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