Enrico Zio

The Laboratory of Analysis of Systems for the Assessment of Reliability, Risk and Resilience (LASAR3) at Politecnico di Milano is offering a fully funded PhD position within the EU-MSCA project RESTORATIVE, focused on the development of generative AI models for intelligent maintenance of thermomechanical energy storage systems. The RESTORATIVE project, funded by the European Union’s Horizon Europe Research and Innovation Programme under the Marie Skłodowska-Curie Grant Agreement No. 101227219, aims to accelerate the green transition by advancing Thermo-Mechanical Grid-Scale Energy Storage Systems (TM-GSES). As a PhD researcher, you will join a multidisciplinary team of 17 doctoral fellows from institutions across Europe, collaborating to bridge technological and policy gaps in the energy sector. Your research will address the challenge of data scarcity in new-design TM-GES systems by developing generative artificial intelligence methods to predict the degradation state of key components. These prediction outcomes will be integrated within an optimization framework for intelligent maintenance, contributing to the reliability and flexibility of grid-scale energy storage. Key responsibilities include developing AI methods for component degradation prediction, optimization models for maintenance planning, and collaborating with project partners for deployment of these methods. Strategic secondments and participation in network-wide PhD training schools covering technical topics such as thermodynamics, entrepreneurship, and reliability engineering are part of the programme. The position offers a competitive gross annual salary of approximately €54,378.36 (pre-tax and social security), including living and mobility allowances according to MSCA rules. An additional family allowance is available if applicable. The employment period is 3 years, with access to a personalized Career Development Plan and a vast international research network. Applicants must have a completed Master’s degree in Engineering, Mathematics, Physics, or related disciplines. MSCA eligibility rules apply: candidates must not already possess a doctoral degree and must not have resided or carried out their main activity in Italy for more than 12 months in the 36 months immediately prior to recruitment. Strong interest in energy systems, AI/Machine Learning, optimization, and decision-making is required, along with good English proficiency. To apply, submit your complete application by email to [email protected] and [email protected] no later than 30 June 2026. Your application should be compiled into a single PDF file containing a motivation letter, CV (with countries of residence/work/study for the past 36 months), academic records (grade transcripts and diplomas in English), and an official description of your institution's grading scale. Applications received after the deadline will not be considered. For further information, visit the project and laboratory websites: Dipartimento di Energia and LASAR3 . The position is based in Milano, Italy, at Politecnico di Milano.

LASAR3 at Politecnico di Milano (Department of Energy) is recruiting for a postdoctoral research contract in process mining, Petri Nets, and semiconductor manufacturing . The project, within the European project MOSAIC , focuses on developing data-driven and interpretable decision models for quality-aware semiconductor manufacturing. The research will use production, logistics, and test data to reconstruct process trajectories, identify route variants, bottlenecks, deviations, and feedback delays, and enrich models with quality and reliability information. Key research themes include process mining , Petri Nets , discrete-event systems , scheduling , optimization , quality improvement , reliability , and decision support . The aim is to connect process execution, test measurements, inspection results, yield indicators, and failure/return information to improve production performance and reliability. Eligibility/preferences: applicants should have expertise in Petri Nets, discrete-event systems, scheduling, and optimization, with interest in manufacturing analytics and interpretable modeling. Funding: 2-year research contract; gross annual remuneration €38,000 (excluding employer charges), approximately €26,000 net per year, paid in 13 installments. Deadline: 2026-08-01. Start date: 2026-10-01. How to apply: submit the application through Politecnico di Milano Online Services under the postdoctoral contracts section and attach all required documents. The direct application link is provided in the post.

Politecnico di Milano’s Department of Energy, through the Laboratory of Analysis of Systems for the Assessment of Reliability, Risk and Resilience (LASAR3), is advertising a postdoctoral contract in Artificial Intelligence for the reliability and maintenance of energy and industrial systems and structures . The research topic is centered on developing AI models for automated anomaly detection , fault diagnosis , and remaining useful life prediction . The project emphasizes advanced methods for handling multi-source data , estimating uncertainty , combining field and simulated data , and producing interpretable outputs that strengthen trust in AI-based decision support for reliability engineering. The work will be carried out within the framework of the PRIME project , funded by INAIL through the BRiC-2025 call. The successful candidate may also collaborate in teaching activities within Master’s degree courses delivered by LASAR3, especially those related to AI for RAMS (Reliability, Availability, Maintainability, and Safety) of industrial systems and plants. Supervisors: Prof. Piero Baraldi and Prof. Enrico Zio. Funding: postdoctoral contract with a gross annual remuneration of €38,000 (excluding employer charges), approximately €26,000 net per year , paid in 13 installments . The contract duration is 2 years . Start date: presumed 01/10/2026. Deadline: 2026-08-03 at 12:00 (Italian time). How to apply: submit the application via Politecnico di Milano Online Services under the postdoctoral contracts section and attach all required documents. The post includes a direct application link.