Objective
Some of the most pressing open problems in modern cosmology concern the nature of the cosmic dark sector, the mismatch between the theoretically predicted and observed values of the cosmological constant, and the growing number of cosmological tensions between independent observational probes. Most notably, the Hubble constant shows a significant and persistent statistical discrepancy between local determinations and those inferred from early-universe Cosmic Microwave Background data. These inconsistencies strongly suggest the need for new physics beyond the standard cosmological model. At the same time, current and forthcoming surveys are generating unprecedented volumes of data, whose accumulation and analysis demand substantial advances in statistical methodology before they can be effectively applied to targeted scientific questions. The MALTA4COSMO project aims to confront these challenges by developing a deep learning framework that incorporates advanced statistical and machine learning techniques, such as neural networks, into cosmological community codes for emulated parameter inference. This approach will enable the identification, in a model-independent way, of generic features of cosmological theories that remain compatible with all available observations. Harnessing the power of deep learning thus provides an innovative arena to explore new physics in the observational sector and to discriminate between competing models that appear degenerate under current methods, advancing data-driven science as a crucial complement to theory-driven approaches. The fellowship will support the researcher’s career development by fostering expertise in machine learning and Bayesian statistics, while also cultivating transversal skills in dissemination and outreach. Moreover, it will expand the researcher’s professional network within Europe and internationally, consolidating a long-term trajectory at the interface of cosmology, data science, and fundamental physics.
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 data science
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
- natural sciences physical sciences astronomy physical cosmology
- natural sciences computer and information sciences artificial intelligence computational intelligence
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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-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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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-PF
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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.
MSD 2080 MSIDA
Malta
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.