Data-driven asset management
Work description
The work is part of the PowerUP Project and focuses on asset management associated with ultra-fast charging infrastructures for heavy-duty vehicles (MCS), which are characterized by high power levels, operational demands, and critical requirements in terms of availability and safety. The main objective is to develop data-driven approaches to support decision-making in asset management, leveraging operational and equipment condition data to improve reliability, availability, and economic efficiency throughout the asset lifecycle. The work includes the analysis of failure modes, the use of monitoring data, and the development of degradation and forecasting models, including the identification of root causes of atypical degradation phenomena. Additionally, it encompasses the development of user-oriented alarm strategies based on natural language, with the aim of facilitating the interpretation of information by operators and decision-makers. In particular, the goal is to assess the impact of these policies in terms of overall asset performance, including availability, operation and maintenance costs, and risk, contributing to the development of advanced asset management capabilities in the context of emerging energy systems.
Academic Qualifications
Enrolment on a PhD programme.
Minimum profile required
Experience in data analysis. Experience in data analysis.
Preference factors
Experience in developing data-driven models for decision support, including the analysis of real-world data, modelling, and solution evaluation. Experience with quantitative methods applied to engineering problems (e.g., optimisation, simulation, statistical analysis, or applied machine learning).
Application Period
Since 15 Oct 2026 to 10 Nov 2026
Centre
Industrial & Systems Engineering and Management