In the Laboratory of Analysis of Systems for the Assessment of Reliability, Risk and Resilience (LASAR³) at Politecnico di Milano we develop methods for risk and resilience assessment, reliability and availability analysis, prognostics and health management and maintenance by leveraging all available knowledge, information and data for the reliability, risk and resilience assessment of components, systems and critical infrastructures.

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PhD Position within EU-MSCA project

RESTORATIVE

Title

Development of Generative AI Models for Intelligent Maintenance of Thermomechanical Energy Storage Systems

Context:

The proposed PhD falls within the project “Grid-scale Energy Storage: Imperatives for Accelerating the Green Transition (RESTORATIVE)” funded by the European Union’s Horizon Europe’s Research and innovation Programme under the Marie Skłodowska-Curie Grant Agreement No. 101227219. RESTORATIVE aims to develop technology for accelerating the green transition through Thermo-Mechanical Grid-Scale Energy Storage Systems (TM-GSES). (https://euraxess.ec.europa.eu/jobs/436154)

The candidate will join the Laboratory of Analysis of Systems for the Assessment of Reliability, Risk and Resilience (LASAR³) at Politecnico di Milano and will conduct research under the supervision of Professors Piero Baraldi and Enrico Zio.

Within the RESTORATIVE project, the PhD researcher will collaborate with a multidisciplinary team of 17 doctoral fellows from institutions across Europe to bridge technological and policy gaps in the energy sector.

About the PhD Research:

The industrial success of TM-GES will depend on their functional reliability to continuously provide stability and flexibility to the grid. To achieve this, methods are needed for the accurate prediction of degradation to inform intelligent maintenance strategies. Artificial Intelligence (AI) can be employed for this, but it requires extensive field data which are unavailable for new-design systems such as TM-GES. In this context, the objective of the PhD research is to contribute to overcoming such data scarcity by developing methods of Generative Artificial Intelligence to predict the degradation state of key TM-GES components. The prediction outcomes are eventually integrated within an optimization framework of intelligent maintenance.

Key Responsibilities:

• Research & Development: develop AI methods to predict component degradation and optimization  models to plan intelligent maintenance.
• Collaboration: Work closely with the other project partners in order to achieve the deployment of the developed methods to TM-GES systems.
• Secondments: To be defined strategically for the benefit of a successful PhD and project.
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Training:

Participate in project network-wide PhD training schools covering technical topics like thermodynamics, entrepreneurship and reliability engineering.

Requirements:

• Educational Background: completed Master’s degree in Engineering, Mathematics, Physics and related disciplines.
• MSCA Eligibility: at the date of recruitment, the candidate must not already possess a doctoral degree.
• Mobility Rule: the candidate must not have resided or carried out your main activity in Italy for more than 12 months in the 36 months immediately prior to your recruitment.
• Skills: strong interest in energy systems, AI/Machine Learning, optimization and decision-making.

Benefits:

• Competitive Salary: A gross annual salary of approximately €54,378.36 (pre-tax and social security). This amount includes living and mobility allowances according to MSCA rules. An additional family allowance will be added if applicable. The period of employment is 3 years.
• Career Development: Access to a personalized Career Development Plan and a vast international research network.

Application procedure:

Please submit your complete application by email to piero.baraldi@polimi.it and dario.valcamonico@polimi.it no later than 30 June 2026 (23:59 Italian time).

Your application must be compiled into a single PDF file containing the following documents:

• A motivation letter.
• Curriculum Vitae (CV): You must clearly indicate the countries where you have lived, worked, or studied over the past 36 months to verify compliance with the MSCA mobility rules.
• Academic Records: Grade transcripts and BSc/MSc diplomas (in English), including an official description of your institution’s grading scale.

Please note: Applications received after the deadline will not be considered.

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