Objective
Energy systems are evolving into highly interconnected multi-carrier networks spanning individual processes and large-scale infrastructures. As complexity increases, model-based optimisation becomes essential for designing and operating such systems under dynamic and uncertain conditions. Physics-based models offer interpretability and physical consistency but are often too computationally demanding for real-time or system-level use. Data-driven models, while efficient at capturing complex behaviours, require extensive datasets and often suffer from limited generalisation and physical coherence. Hybrid modelling approaches that blend physical knowledge with machine learning therefore represent a promising path.
However, experts in energy systems typically master physics and engineering principles but lack advanced machine-learning skills. Conversely, data-science specialists excel in AI techniques but often lack the domain knowledge required to handle energy systems and their operational constraints.
MOBIDIC bridges this gap by training a new generation of researchers who operate at the interface between energy systems engineering and AI. The 15 doctoral candidates advance methodologies for modelling, optimisation, uncertainty handling, and integration, each working on a use case at process, microgrid, or network scale. MOBIDIC ultimately delivers a coherent toolbox of solutions to support the design and control of future energy systems.
By being trained at the intersection of physics-based modelling, numerical simulation, data analytics, optimisation, and uncertainty-aware decision-making, competences rarely combined in traditional curricula, and by working with industrial datasets and operational constraints, the MOBIDIC cohort will be able to bridge fundamental multiphysics modelling and deployable digital tools, ensuring immediate employability in academia, industrial R&D, and system operations.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences computational science multiphysics
- natural sciences computer and information sciences artificial intelligence machine learning
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-DN - HORIZON TMA MSCA Doctoral Networks
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2025-DN-01
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
91120 Palaiseau
France
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.