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Crea una Job Alert gratuita per doctoral candidate position at unibo in the restorative msca doctoral network:on "ex-ant[...] / italia

Doctoral Candidate position at UNIBO in the RESTORATIVE MSCA Doctoral Network:on "Ex-Ant[...]

Pubblicato il 13-08-2026 - Alma Mater Studiorum - Universita' Di Bologna in Italia

Organisation/Company Alma Mater Studiorum - Università di Bologna Department Department of Civil, Chemical, Environmental, and Materials Engineering - DICAM Research Field Engineering » Chemical engineering Engineering » Systems engineering Technology » Energy technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 31 Aug 2026 - 23:59 (Europe/Rome) Country Italy Type of Contract Temporary Job Status Full-time Hours Per Week 40 Offer Starting Date 1 Nov 2026 Is the job funded through the EU Research Framework Programme? Horizon Europe - MSCA Reference Number DC14 UNIBO_RESTORATIVE Marie Curie Grant Agreement Number 101227219 Is the Job related to staff position within a Research Infrastructure? No

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Offer Description
The successful candidate for this post (DC14)



will be employed full-time for a fixed-term of 36 months and enrolled on a PhD at the University of Bologna from 1st November 2026 to research and write a PhD thesis on Ex-Ante LCA tools for the environmental impact assessment of TM-GSES systems.

Job duration: 36 months

Main research field: Chemical Engineering

Research subfield: Systems Engineering, Life Cycle Assessment, Energy Storage

The project aims at developing an innovative prospective (“ex-ante”) life cycle assessment (LCA) approach for the quantitative evaluation of the sustainability of grid‑scale energy storage (GSES) technologies.

It will define the scope and boundaries of the ex‑ante LCA within a cradle‑to‑grave perspective, with the goal of creating a novel framework for assessing environmental impacts of energy storage technologies, with particular reference to thermo‑mechanical GSES (TM‑GSES) systems.

The approach will incorporate a forward‑looking perspective into life cycle modelling, by addressing uncertainties related to technological scaling and learning, as well a

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