KTP Associate in AI-Powered Predictive Maintenance

This Knowledge Transfer Partnership, between Aston University and Genie UK Limited, offers an opportunity to lead the development of an advanced AI-driven predictive and prescriptive maintenance system for Genie's lifting equipment. The project focuses on connected, partially connected, and non-connected machines, transforming telematics, onboard sensor data, and historical maintenance data into actionable maintenance intelligence within Genie's LiftConnect platform.

You will work on a sector-leading project, with responsibility for developing intelligent data-quality agents, predictive models that can transfer across machine families, prescriptive decision support, and operational dashboards. You will also ensure delivery of methodologies, documentation, training, and workshops needed to embed the solution in the business.

You will lead a strategic project of direct strategic and commercial importance to the business. The role offers experience of developing a minimum viable product, working with subject matter experts from service, project management, organisational change, parts, data, telematics, and commercial teams, and contributing to the launch of a new predictive maintenance service. You will receive day-to-day guidance from business and academic supervisors, along with access to Aston's KTP Associate network, and a dedicated personal development budget.

You will work in a project team with Prof Abdul Sadka and Dr Chao Liu from Aston University and the Senior Management Team at Genie UK, with further support from an Innovate UK Knowledge Transfer Adviser.

About the Business Partner: Genie is a world-leading manufacturer of equipment that solves customers' aerial worksite challenges with five decades of industry leadership. It has a global presence supplying and maintaining a range of lifting equipment solutions from boom and scissor lifts to portable aerial work platforms. Its UK location is focused on after-care, and this project offers the opportunity to play a leading role in a strategically important project for data-led aftermarket services.

Essential skills and experience:
- Demonstrable experience in software tools for data analysis, such as SQL, Power BI, and cloud-based analytics environments
- Feature extraction and time-series analysis
- Probabilistic prediction techniques using scalable Python-based development environments such as PyTorch or TensorFlow
- Cloud-native machine learning and MLOps environments such as AWS SageMaker
- Small or large language model development with retrieval-augmented generation

Desirable skills and experience:
- Industrial analytics or predictive maintenance
- Knowledge of construction-related industries and market drivers
- Commercial awareness to connect technical development with business impact, budget awareness, and resource management

Education: PhD level in a relevant field such as Artificial Intelligence, Machine Learning, Computer Science, Data Science, Control Engineering, or a related subject, with demonstrable project experience in a technically demanding area.

Attributes: Strong research capability and the motivation to guide a technically complex project. Excellent communication skills to engage with stakeholders at various levels of technical knowledge. Effective project and time management skills to facilitate a staged implementation. Ability to transform complex modelling work into deployable industrial solutions.

Location: Predominantly based at Genie UK's premises in Grantham, with access to facilities at Aston University in central Birmingham. Some travel to key clients may be required. A hybrid working model is in operation. Candidates must live within a commutable distance.

Benefits:
- £2,000 per annum personal and professional development budget
- 25 days annual leave per annum
- Professional support and mentorship
- Mental health and wellbeing support via Aston Wellbeing
- 60% of KTP Associates are offered employment by their host companies at the end of the KTP