Data-driven asset management and decision support for maintenance
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
PhD degree in Industrial Engineering and Management, Mechanical Engineering, Electrical Engineering, Systems Engineering, or a related field.
Minimum profile required
PhD diploma certified by DGES.Experience in programming for data analysis, including the handling and analysis of real-world data, using tools such as Python, R, or equivalent. Experience in Project management.Proficiency in Portuguese and English.
Preference factors
- Experience in asset management, maintenance or reliability analysis, particularly in industrial or infrastructure contexts. - Experience in developing data-driven models for decision support, including the analysis of real-world data, modelling, and solution evaluation. - Experience in the definition and evaluation of maintenance policies (e.g., preventive, condition-based, or predictive maintenance). - Experience with quantitative methods applied to engineering problems (e.g., optimisation, simulation, statistical analysis, or applied machine learning). - Participation in R&D projects with links to real-world or industrial contexts. - Relevant scientific publications in the areas of asset management, maintenance, reliability, or related fields.
Application Period
Since 15 May 2026 to 28 May 2026
Centre
Industrial & Systems Engineering and Management