Role Summary
University of Derby’s College of Science and Engineering, in partnership with engineering consultancy Saith Ltd, offers an exciting career‑development opportunity. You will manage and deliver a strategic Knowledge Transfer Partnership (KTP) project based at Saith’s premises in Hampshire while employed by the University.
You will lead the design, development, and deployment of a practical, production‑ready AI‑enabled Digital Twin platform to support intelligent asset management within energy and utility infrastructure. At the interface of research and industry you will translate advanced AI, computer vision, and data‑driven methods into scalable solutions deployed within live engineering environments.
Responsibilities
- Translate stakeholder and operational needs into technical system designs through co‑design workshops and engagement with engineers, clients, industry partners, and academics.
- Ensure solutions align with client requirements and deliver measurable operational and commercial value.
- Design and maintain data pipelines and infrastructure for ingesting, processing, and managing spatial, temporal, and operational data.
- Process and integrate BIM, LiDAR, and point‑cloud data into consistent and usable Digital Twin environments.
- Develop and apply machine learning and computer vision models for component recognition, anomaly detection, compliance monitoring, and predictive maintenance.
- Design and implement an AI‑enabled Digital Twin system integrating multi‑source data for infrastructure asset management.
- Work closely with design, construction, and operational teams to integrate and validate solutions in live projects and real‑world environments.
- Develop simulation and risk analysis tools to support inspection, maintenance planning, and operational decision‑making.
- Create dashboards and decision‑support tools to visualise data and communicate actionable insights.
- Lead pilot deployment, system testing, and performance evaluation in real‑world environments.
- Drive adoption of AI and Digital Twin technologies within Saith through training, documentation, and embedding of new workflows across the business.
- Deliver practical, deployable outputs within the KTP timeframe, prioritising implementation and operational impact.
- A Master’s degree in Artificial Intelligence, Computer Vision, Computer Science, Software Engineering, or a closely related discipline.
Experience
- Experience of translating business or operational requirements into technical solutions.
- Experience of working with complex or multi‑modal datasets, including spatial, temporal, 3D, LiDAR, point cloud, BIM, or sensor data.
- Experience of data engineering and pipeline development, including Extract, Transform, and Load processes.
- Experience in computer vision and visual AI, including classification, detection, segmentation, or anomaly identification tasks.
- Experience of developing and validating machine learning models, including performance evaluation using structured datasets and/or SQL‑based systems.
- Experience of designing and delivering end‑to‑end AI, analytics, and Digital Twin solutions, including deployment and integration with user‑facing applications (e.g., dashboards).
- Experience of Digital Twin or simulation‑based systems.
- Experience of optimisation, simulation, or scenario‑based modelling approaches.
- Experience of deploying AI or analytics solutions in cloud or production environments (e.g., AWS, Azure, or similar).
- Experience of applying computer vision techniques in real‑world or industrial scenarios.
- Experience of working with engineering or spatial datasets, such as BIM, LiDAR, point‑cloud, or 3D data.
- Experience of working with annotated visual datasets for computer vision tasks in infrastructure, inspection, or engineering contexts.
Skills, knowledge and abilities
- Strong programming skills in Python for data engineering and machine learning, with familiarity in computer vision frameworks (e.g., OpenCV, PyTorch, TensorFlow, or similar).
- Sound understanding of machine learning workflows, including model design, development, validation, and performance evaluation.
- Understanding of computer vision workflows, including classification, detection, segmentation, or related recognition tasks.
- Ability to design and implement scalable analytical or AI‑based solutions for real‑world environments.
- Be conversant with version control (e.g., Git) and writing reproducible, well‑documented code.
- Understanding of data pipelines, cloud‑based systems, or distributed data processing environments.
- Familiarity with data visualisation and dashboard tools for communicating insights.
- Experience handling large, complex, or multi‑modal datasets in operational settings.
- Excellent written and verbal communication skills, with the ability to present complex ideas to both technical and non‑technical audiences.
- Ability to work independently and collaboratively within multidisciplinary teams.
- Strong organisational skills with the ability to prioritise tasks and meet deadlines.
- Results‑driven problem‑solving ability with a pragmatic and solution‑oriented approach.
- Alignment with organisational values and professional conduct standards.
- Working knowledge of AI system architecture and deployment, including model lifecycle, monitoring, or MLOps practices.
- Understanding of data governance, regulatory requirements (e.g., GDPR), and operational best practices.
- Ability to consider scalability, performance optimisation, reliability, and maintainability in applied AI systems.
- Working knowledge of BIM, LiDAR, or 3D/spatial data workflows.
- Ability to integrate AI outputs into dashboards, business intelligence tools, or decision‑support systems.
Business Requirements
- This is an office‑based role requiring a consistent and visible presence within the office to support effective communication, collaboration, and project delivery. Travel to client offices, project sites, and other business locations may be required in line with operational needs.
- Willingness to work flexibly and respond to project needs and stakeholder expectations.
- Willingness to travel between project sites across the UK and Ireland (if required).
Eligibility for Sponsorship
The offered salary for this role is less than the going rate for the occupation and the minimum salary threshold for the Skilled Worker route (£41,700 per annum, as of 22nd July 2025). Therefore you will only be eligible to apply for a Skilled Worker visa subject to your individual circumstances, which must meet one of the following criteria set out by UKVI:
- Your job is on the Immigration Skills List.
- You’re under 26, studying or a recent graduate, or in professional training.
- You have a PhD level qualification that’s relevant to your job.
- You have a postdoctoral position in science or higher education.
Please note that the University will assess your individual eligibility for sponsorship at the shortlisting stage.
EEO Statement
The University of Derby is committed to promoting equity, diversity and inclusion, regardless of age, disability, trans status, marriage and civil partnership, pregnancy and maternity, race, religion or belief (or none), sex and sexual orientation. We are Disability Confident Employers and also commit to ensuring an environment which is trans and non‑binary‑inclusive for all staff, students, partners and visitors. We invite applicants to highlight adjustments they may require to ensure equitable participation. We also offer the option to choose preferred titles and pronouns as part of the recruitment process.
Contact
For further information and informal enquiries regarding the role, please contact Dr Oluwarotimi W. Samuel, Senior Lecturer in Computer Science via o.samuel@derby.ac.uk or Dr Mojisola Grace Asogbon, Lecturer in Data Science via m.asogbon@derby.ac.uk. For enquiries regarding your application and for sponsorship eligibility, please contact the recruitment team via recruitment@derby.ac.uk.
