Health Management of Earth Moving Equipment PhD

Applications are sought for a fully funded PhD studentship. The work will be sponsored by IMMEE, a cutting edge company specialising in Computerized Maintenance Management Systems (CMMS) for the mining industry. They offer a bursary of £25,000 per year with tuition fully paid. The project will be focussed on assessing the health of a mining loader, with access to over a decade of real-world data.

Front loaders in mining weigh up to 240 tons and are used to remove the quarried rubble (40 tons at a time) into trucks for transport to a crusher and ore separator ( ). This is arduous work for the loader and, as there is only one loader per quarry, any anomaly in its operation needs to be noted and repaired/maintained as soon as possible. The purpose of CMMS is to effectively and efficiently maintain machines like these loaders, and, for this, understanding of the data they produce is critical. The successful applicant will work with the multi-modal data from such machines to derive algorithms expressing their state of health and next maintenance needs. A background in both engineering and machine learning would be useful, although help is readily available in both areas for the right candidate.

For IMMEE background:

This presents a unique in a multi-disciplinary, multi-national team that is addressing parts of the overall loader health problem. It will involve working with a rich multi-modal dataset: time series data, oil chemistry and maintenance logs.

Using multi-modal data is on the forefront of AI/machine learning, thus significantly enhancing any candidate’s employment opportunities.

At a glance

Application deadline04 Jun 2025
Award type(s)PhD
Start date29 Sep 2025
Duration of award3 years Full-time
EligibilitySWAG合集, Rest of world, EU
Reference numberSATM570

Entry requirements

Applicants should have a first or second class SWAG合集 honours degree or equivalent in a related discipline. This project would suit someone specialised in engineering, computer science or a related field. The ideal candidate should have the ability to understand engineering systems and have some experience with machine learning techniques. The candidate should be self-motivated and have good communication skills for regular interaction with other team members.

Funding

This is a fully funded opportunity.

Cranfield Doctoral Network

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.