Objective
While AI for science has enabled remarkable breakthroughs in recent years, many scientists have been left underwhelmed when trying to apply AI methods in their domains. This is mainly because most scientific datasets are too small for the data-hungry modern foundation model approaches. Bayesian deep learning (BDL), combining rigorous Bayesian principles with the flexibility of neural networks, has positioned itself as a powerful remedy for this lack of data, but its adoption is hampered by some tedious design choices, most importantly, the selection of model, prior, and inference method. This proposal aims to bring the revolution of automated machine learning, which has radically boosted the adoption of conventional AI methods, into the realm of BDL. The goal is to develop an automated end-to-end pipeline that domain practitioners can autonomously use to select the best model architectures and data invariances, incorporate their own prior knowledge in an efficient and natural way, and choose the best inference method tailored to their computational resources. While many research challenges will have to be overcome to reach this ambitious goal, recent advances in BDL research, including some of my own, put me in the ideal position now to tackle these questions. Throughout this process, the research will stay grounded in real-world scientific requirements via close collaboration with domain experts in healthcare, materials science, and astrophysics. A successful completion of this project will democratize access to BDL solutions by cutting out the machine learning middlemen, thus unlocking small-data AI applications across scientific domains.
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.
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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.1.1 - European Research Council (ERC)
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-ERC - HORIZON ERC Grants
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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) ERC-2026-STG
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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.
90461 NUREMBERG
Germany
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.