About this role:
This Knowledge Transfer Partnership, between Aston University and Genie UK Limited, offers an opportunity to oversee 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 contribute to a sector-leading project, with responsibilities that include developing intelligent data-quality agents, predictive models that can transfer across machine families, prescriptive decision support, operational dashboards, and ensuring the delivery of methodologies, documentation, training, and workshops needed to integrate the solution into the business.
What you will gain:
You will manage a strategic project of direct importance to the business. The role provides experience in developing a minimum viable product, collaborating 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 to support your professional growth throughout the project.
What we are looking for:
You should be educated to PhD level in a relevant field such as Artificial Intelligence, Machine Learning, Computer Science, Data Science, Control Engineering, or a related subject, and you should be able to demonstrate project experience in a technically demanding area. Skills and experience required for this exciting role include:
Essential:
- 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:
- 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
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 and explain complex concepts clearly. Effective project and time management skills to facilitate a staged implementation. Ability to transform complex modelling work into deployable industrial solutions.
Additional Benefits and Support:
£2000 per annum for personal and professional development for the duration of the project
Annual leave (25 days p/a)
Professional support and mentorship
Mental health and wellbeing support: Aston Wellbeing
Career prospects:
KTP Associates manage strategic projects, bridging the academic and business worlds, which can enhance and fast-track their careers. You will also benefit from expert coaching and mentoring. 60% of our KTP associates are offered employment by their host companies at the end of the KTP.
About this role:
This Knowledge Transfer Partnership, between Aston University and Genie UK Limited, gives you the chance 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, turning 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, operational dashboards and ensure delivery of methodologies, documentation, training and workshops needed to embed the solution in the business.
What you will gain:
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
This is a Knowledge Transfer Partnership (KTP) funded by Genie UK Limited and Innovate UK. KTPs are collaborative, three-way partnerships creating positive impact and driving innovation by linking businesses with the UK's world class knowledge bases to deliver innovation projects led by skilled graduates. It is essential you understand how KTP works and the vital role you will play if you secure this position. To learn more please visit: www.aston.ac.uk/ktp
Aston University: 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.
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 focussed on after-care, and this project offers the opportunity to play a leading role in a strategically important project for data-led aftermarket services.
Location: The Business Partner operates a hybrid working model but you will be based predominantly at Genie UK’s premises in Grantham. You will also have access to facilities at Aston University in central Birmingham. Some travel to key clients may also be required. Therefore, you must live within a commutable distance.
To learn more about this role it is essential you refer to the Job Description documents. For informal enquiries about this role please contact Dr Chao Liu e-mail: c.liu16@aston.ac.uk.
If you think this isn’t the right opportunity for you, please explore other KTP vacancies across the country: https://www.ktp-uk.org/jobs/
Aston University is committed to the principles of the Athena SWAN Charter http://www.ecu.ac.uk/equality-charter-marks/athena-swan . We pride ourselves on our vibrant, friendly, supportive working environment and collaborative atmosphere.
Please visit our website https://jobs.aston.ac.uk/Vacancies.aspx for further information and to apply online.
As users of the disability confident scheme, we guarantee to interview all disabled applicants who meet the minimum criteria for the vacancy.