Project description
Beyond the black box: transparent neuro-symbolic AI
While deep learning excels at pattern recognition, its ‘black box’ nature lacks the transparency required in high-risk fields such as healthcare. Supported by the Marie Skłodowska-Curie Actions programme, the NeSySCALE project aims to bridge this gap by combining neural networks with the structured logic of symbolic AI. By integrating these approaches through neural probabilistic programming, the project will create models that are both powerful and interpretable. To overcome current technical limitations, it will employ a dynamic depth-first search to ensure the system remains scalable and reliable. Ultimately, NeSySCALE seeks to provide an end-to-end framework for trustworthy AI, enabling clear, explainable reasoning in critical sectors where accountability is essential for safety and public trust.
Objective
Deep learning has achieved remarkable success in tasks such as language generation, image classification, and speech recognition. However, these models remain largely opaque “black boxes,” making it difficult to understand or explain their predictions. This lack of interpretability limits their use in high-risk, regulated domains like healthcare or autonomous systems, where transparency and accountability are essential. The field of neuro-symbolic AI addresses this limitation by combining deep learning’s pattern recognition capabilities with the structured reasoning of symbolic AI. Neural probabilistic programming (NPP) is one such approach, integrating neural networks with probabilistic logic programs to produce models that are both powerful and interpretable. For example, in hospital risk monitoring of sepsis, NPP systems can transform raw medical data into probabilistic observations and then reason reliably using logic programs, making predictions transparent and explainable. A major challenge of NPP is scalability, as symbolic reasoning relies on computationally expensive weighted model counting, which is an obstacle to its application in practice. The NeSySCALE project aims to overcome this by developing a neuro-symbolic framework that integrates learning and reasoning in an end-to-end manner while remaining scalable, robust, and reliable. Using a dynamic depth-first search approach with formal guarantees on approximation errors, the project will enable trustworthy AI in domains where standard neural networks are currently unsuitable. The PF will join the DTAI research group at KU Leuven, under the supervision of Professors Giuseppe Marra and Luc De Raedt.
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 artificial intelligence machine learning deep 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-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.
3000 Leuven
Belgium
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.