The successful candidate will lead the development of advanced machine learning models for predictive maintenance in railway systems, working closely with MoniRail Ltd and Durham University. The primary focus will be on designing and implementing deep learning and anomaly detection algorithms to analyse large-scale, real-world sensor data collected from in-service trains. This data will be used to identify early signs of track and vehicle degradation, to allow for a shift from reactive to condition-based maintenance.
The candidate will be expected to carry out high-quality research at the intersection of AI, signal processing and applied railway engineering. They will collaborate with MoniRail’s development and engineering teams to integrate developed models into the company’s existing solutions, so the outputs are scalable, reliable and deployable in real-world operational settings.
In addition, the candidate will adhere to the following responsibilities:
· Develop a wide range of skills within the cutting edge of computer science, through studies in state-of-the-art research, lectures and seminar attendance.
· Develop technical expertise in machine learning, predictive modelling and sensor data analytics within a transport engineering context.
· Implement state-of-the-art solutions and identify solutions to technical problems.
· Contribute to the planning and execution of the KTP workplan to deliver on defined technical milestones.
· Research, prototype and validate models using MoniRail’s datasets and publicly available data and ensure that they are up to the company’s and university’s standards.
· Communicate progress through regular project meetings and written reports.
· Attend regular project meetings and periodic evaluations
· Work with developers to prepare code for deployment and support product integration.
· Produce technical documentation, user guides and internal training materials.
· Contribute to academic outputs, including drafting research papers and conference presentations and participate in dissemination activities.
The KTP Associate will lead a Knowledge Transfer Partnership (KTP) project that is a collaboration between Durham University and MoniRail Ltd based in Birmingham. The Knowledge Transfer Partnership (KTP) scheme helps businesses to innovate and grow through the aid of discipline specific academic expertise. It does this by linking them with an academic supervisory team and a researcher in a university to work on a specific project.
Working alongside a close-knit team of developers and engineers, the KTP Associate will lead an innovative project to design, develop and implement predictive machine learning models for track and vehicle degradation using cutting-edge deep machine learning, and will integrate these into MoniRail’s real-time monitoring system to deliver intelligent, data-driven maintenance insights.
About the business
MoniRail Ltd is a pioneering UK-based company specialising in non-intrusive, in-service railway condition monitoring. MoniRail leverages over 20 years of cutting-edge research to deliver innovative solutions for the rail industry. Their system utilises lightweight Inertial Measurement Units installed on operational passenger and freight trains to continuously monitor track geometry, ride comfort and vehicle performance. This approach enables real-time data collection without disrupting regular rail services, which enables early detection of track degradation and facilitating predictive maintenance strategies.
#J-18808-Ljbffr
