Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Machine learning in science and society: A dangerous toy? (TOY)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Texts and Concepts
Panel description: Literary studies , literature,  philosophy
Countries involved: Netherlands
Call: ERC STARTING GRANTS (ERC-2024-STG, 2024)
Start date: 2025-01-01
End date: 2029-12-31
Duration (months): 60.0
Total project cost (EUR): 1500000.0
EU funding (EUR): 1500000.0
Participants: 1
Project status: SIGNED

Deep learning (DL) models are encroaching on nearly all our knowledge institutions. Ever more scientific fields—from medical science to fundamental physics—are turning to DL to solve long-standing problems or make new discoveries. At the same time, DL is used across society to inform and provide knowledge. We urgently need to evaluate the potentials and dangers of adopting DL for epistemic purposes, across science and society. This project uncovers the epistemic strengths and limits of DL models that are becoming the single most way we are structuring all our knowledge, and it does so by starting with an innovative hypothesis: that DL models are toy models. A toy model is a type of highly idealized model that greatly distorts the gritty details of the real world. Every scientific domain has their own toy models that are used to "play around" with different features, gaining insight into complex phenomena. Conceptualizing DL models as toy models exposes the epistemic benefits of DL, but also the enormous risk of overreliance. Since toy models are so divorced from the real world, how do we know they are not leading us astray? TOY addresses this fundamental issue. TOY 1) identifies interlocking model puzzles that face DL models and toy models alike, 2) develops a theory of DL (toy) models in science and society based on the function of their idealizations and 3) develops a philosophical theory for evaluating the epistemic value of DL (toy) models across science and society. In so doing, TOY solves existing problems, answers open questions, and identifies new challenges in philosophy of science, on the nature and epistemic value of idealization and toy models; in philosophy of ML, by looking beyond DL opacity and developing a philosophical method for evaluating the epistemic value of DL models; and by bringing siloed debates in ethics of AI together with philosophy of science, providing necessary guidance on the appropriate use and trustworthiness of DL in society.

Keywords: machine learning in science,scientific understanding,scientific explanation,idealization,models in science,toy models,values in science,epistemic value
Learning the interaction rules of antibody-antigen binding (AB-AG-INTERACT)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Biotechnology and Biosystems E ngineering
Panel description: Biotechnology using all organisms , biotechnology for environment  and food applications, applied plant and animal sciences , bioengineering and synthetic biology , biomass and biofuels , biohazards
Countries involved: Norway
Call: ERC CONSOLIDATOR GRANTS (ERC-2023-COG, 2023)
Start date: 2024-01-01
End date: 2028-12-31
Duration (months): 60.0
Total project cost (EUR): 2000000.0
EU funding (EUR): 2000000.0
Participants: 1
Project status: SIGNED

Antibody-antigen binding is the basis of two fundamental biotherapeutic pillars: monoclonal antibodies (1) and vaccines (2). To accelerate therapeutics discovery, we need to perform antibody (Ab) and antigen (Ag) design in silico. Specifically, we need to address a fundamental immuno-biotechnological challenge: understanding the interaction rules that predict Ab-Ag binding. Solving this challenge demands the convergence of biotechnology, computational structural biology, and machine learning (ML). My lab is one of the few worldwide to have this transdisciplinary expertise. Research problem: Currently, the predictive performance of Ab-Ag binding is poor, and an understanding of the underlying rules of Ab-Ag binding is mostly absent. We previously showed that both unprecedentedly large datasets (>10^5 Ab-Ag sequence pairs) and extensive structural information on the Ab-Ag binding interface (paratope, epitope) are needed to increase prediction accuracy and recover binding rules. Targeted breakthrough: To address the lack of large-scale Ab-Ag sequence and structural data, we will develop a method for high-throughput screening of >10^3 Ab paratope-mutated variants binding to >10^3 of Ag epitope-mutated variants, generating sequence data of Ab-Ag binding pairs at an unprecedented scale (>10^6 sequence Ab-Ag pairs). Structural information of the entirety of the sequence-based Ab-Ag binding data will be generated by building and innovating on recent breakthroughs in computational structural biology. To derive Ab-Ag interaction rules from the generated data, we will develop ML techniques for Ab-Ag binding prediction and rule recovery. We will demonstrate experimentally that we have begun to understand Ab-Ag interaction rules. Impact: The proposed research generates the exact data necessary to recover the rules of Ab-Ag binding and provides a first groundbreaking insight into those rules, moving us closer to in silico on-demand antibody and vaccine design.
Natural Traces in forensic investigations - how the analysis of non-human evidence can solve crime (Natural Traces)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Doctoral Networks
Action type: Marie Skłodowska-Curie actions
Panel: Environment and Geosciences
Countries involved: Germany,Czechia,Hungary,Argentina,United Kingdom,Portugal,Latvia,Poland,Netherlands,Bulgaria,Spain,Estonia,Italy,Sweden
Call: MSCA Doctoral Networks 2022 (HORIZON-MSCA-2022-DN-01, 2022)
Start date: 2024-03-01
End date: 2028-02-29
Duration (months): 48.0
Total project cost (EUR): 2455941.6
EU funding (EUR): 2455941.6
Participants: 26
Project status: SIGNED

Literally any crime scene contains animal, plant, soil, microbial or environmental traces. Examples include pet hairs or saliva, pollen and algae, soil and soil organisms like bacteria or nematodes, fungal spores, insects, and environmental traces in the form of environmental DNA of any origin. Such biological traces can be found mainly on living people (suspects, victims), on objects like cars or weapons, and on bodies not just on the surface but also in their surroundings like single or mass graves, or an aquatic environment. In the EU, there is a lack of training opportunities in applied topics of forensic science at postgraduate level, such as the qualitative and quantitative analysis of specifically such non-human biological traces. While many EU countries have forensic laboratories that also such traces, a much broader, integrated knowledge base is needed, built on close cooperation between basic research in academia and analysis and application in casework by forensic specialists. Natural Traces aims to optimise the procedures for taking and analysing non-human traces in order to meet the ever-increasing demands for identifying suspects and victims and to link traces to different places, times or courses of action. The results based on this need a statistical robustness that will also hold up in the courtroom under cross-examination. The proposal aims for 10 PhD students with a background in life sciences. They will build their knowledge and experience in practical forensics in training schools provided by academic, non-academic partners, and police academies or laboratories. The training embeds biological evidence in a Bayesian framework, making it universally comparable with all other forms of evidence, by applying novel probabilistic approaches to extract forensic useful information from metadata. It is truly cross-sectoral, with secondments at end-users of the training (e.g. police forces) and joint supervision by PIs from different countries.

Keywords: forensic science,taphonomy,graves,soil analysis,mycology,fungi,botany,diatoms,pollen,zoology,insects,mites,edna,microbiome,machine learning,bayesian,
Deliberative improvement of oral care quality (DELIVER)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Health
Theme: Health
Destination: Ensuring access to innovative, sustainable and high-quality health-care
Action type: Research and Innovation actions
Countries involved: Netherlands,Germany,United Kingdom,Sweden,Denmark,Portugal,Malta
Call: Enhancing quality of care and patient safety (HORIZON-HLTH-2021-CARE-05, 2021)
Start date: 2022-08-01
End date: 2026-07-31
Duration (months): 48.0
Total project cost (EUR): 4252852.5
EU funding (EUR): 4252851.5
Participants: 11
Project status: SIGNED

Oral diseases and conditions affect more than 3.5 billion people worldwide. They are the 3rd most expensive diseases to treat in the EU and disproportionally affect vulnerable groups. In deviation from the UN and WHO goal of Universal Health Coverage, many EU citizens do not have access to quality oral care without financial hardship. To this end, the DELIVER (DELiberative ImproVEment of oRal care quality) project aims to enhance the quality of oral care through deliberative dialogue and action involving citizens, patients, providers, payers and policymakers. DELIVER will create a synergistic problem-solving ecosystem to convert deliberative dialogues into meaningful improvement of oral care quality. DELIVER brings together top investigators from prestigious universities, civil society and patient organizations, health professionals, policymakers, and SME/industry to achieve a step change in collective problem solving. Given the comprehensiveness of the topic, DELIVER uses a targeted approach that is entirely focused on five major root-causes underlying the current limitations of oral care systems. Using a mixed-methods research approach, DELIVER will co-develop and co-produce new quality improvement approaches in three phases. The 1st phase involves situational analysis, consenting of core quality indicators, and development of a EU-wide monitoring framework. The 2nd phase involves in-depth analysis of select quality improvement approaches: (i) PROMs/PREMs-based quality improvement in dental practices; (ii) community-based quality improvement for vulnerable groups; (iii) quality-oriented commissioning of oral health services. The regulatory determinants of oral care quality improvement will be scrutinized. In the 3rd and final phase, the knowledge gained in the 1st and 2nd phases will be merged into the DELIVER Quality Toolkit with manuals and digital tools for concretely actionable and context-adaptive approaches for oral care quality improvement.

Keywords: public health,oral health,dentistry,quality improvement,universal health coverage
Multi facEteD ImplementAtion of a mixed sofTwarE/hardware-based zero-trust framework for the computing continuum (MEDIATE)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Civil Security for Society
Theme: Civil Security for Society
Destination: Increased Cybersecurity
Action type: Innovation actions
Countries involved: Greece,Spain,Cyprus,Italy,France,Ireland,Serbia,Switzerland,Germany
Call: Secure Computing Continuum (IoT, Edge, Cloud, Dataspaces) (HORIZON-CL3-2023-CS-01, 2023)
Start date: 2024-11-01
End date: 2027-10-31
Duration (months): 36.0
Total project cost (EUR): 4925505.0
EU funding (EUR): 4061889.75
Participants: 15
Project status: SIGNED

Technological advances in Information communication Technologies (ICT) as well as the digital transformation of complex systems have led to the development of novel networks, platforms and systems, which have, in turn, kick-started the realisation process for multi-faceted technological collaborations and data-driven workflows on a scale never seen before. Consequently, an integration process among the different layers of complex systems and services has been unfolding raising significant issues and challenges in its wake. Hence, the digital data and collaboration spaces are all predicated upon the realisation of what is known as the computing continuum, which is based on the integration of cloud, edge and Internet of Things (IoT). Nevertheless, this has given rise to significant security and privacy risks especially since it is about systems that involve a high number of entities and devices with different profiles, processing a vast amount of potentially sensitive info MEDIATE’s vision is to produce a robust technology, which will address the security and privacy attributes of the computing continuum. For this, it will put forth a complex architecture that is based on the concept of zero-trust and will assume a federated learning approach in order to perform security-based scrutinisation at all continuum levels. i.e. IoT, edge and cloud, using security models that can be updated, redistributed and reconfigured across it. The actual features of the MEDIATE framework will support major topic outcomes such as cybersecurity resilience through reconfiguration, vulnerabilities mitigation through cyber threat analysis, secure integration at the IoT level through software and hardware-based security sensors and trust and security for massive ecosystems through the use of federated learning-based orchestration. Moreover, it will feature AI-based tools for cyber threat intelligence that assist a decision support system and privacy policies for data and identity protection.

Keywords: computing continuum,ai threat intelligence,reconfigurable hardware,security sensors,zero trust,root of trust
EpiGrid: Soft and flexible high density electrode grids for epilepsy surgery (EpiGrid)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Countries involved: Netherlands,Switzerland
Call: ERC PROOF OF CONCEPT GRANTS1 (ERC-2022-POC1, 2022)
Start date: 2023-03-01
End date: 2024-08-31
Duration (months): 18.0
Total project cost (EUR): 174857.14
EU funding (EUR): 150000.0
Participants: 3
Project status: CLOSED

Epilepsy is a highly prevalent brain disorder affecting 1% of the population. Brain surgery can cure epilepsy if we can delineate the diseased tissue intra-operatively. Unfortunately this delineation is currently not performed precisely and leads to poor success rates (50-70%). High frequency oscillations (HFOs) have been identified as a biomarker of epileptogenic tissues, which can be electrically recorded with intraoperative electrocorticography. These HFOs are specific of diseased epileptogenic brain tissues and remnance after an initial resection predicts continuation of seizures after surgery and thus may indicate incomplete removal. The problem is that currently available electrode grids do not provide an adequate recording resolution (too low electrode density). Moreover these electrodes are made of stiff materials, which yields low signal-to-noise level when recording from the cortical surface and which does not offer the possibility to record from within resection cavities, as the rigid electrodes cannot conform to the curvature of the cavity. We need high density flexible electrode grids that can adhere to the cortical surface and cavities. Neurosoft Bioelectronics produces flexible and high density electrode grids based on unique stretchable soft electrodes. Our goal is to use this grant to translate our clinical needs into an optimal design of soft electrode grids adapted for epilepsy surgery, with the help of an industrial designer. We will first test these newly designed soft electrodes in vitro for clinical handability and improved signal quality, and eventually test them during epilepsy surgery in 12 patients. The recorded signals and clinical outcomes will be compared to those from standard rigid electrode grids. Improved delineation of the epileptogenic tissue will lead to a higher succes rate, make epilepsy surgery a first choice treatment, and over all change this otherwise life-long disease into a curable disorder for many people.
Multimodal multitAsk learninG for MultIsCale BATHYmetric mapping in shallow waters (MagicBathy)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Postdoctoral Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Germany,Czechia
Call: MSCA Postdoctoral Fellowships 2021 (HORIZON-MSCA-2021-PF-01, 2021)
Start date: 2023-02-01
End date: 2025-08-31
Duration (months): 31.0
Total project cost (EUR): 189687.36
EU funding (EUR): 189687.36
Participants: 2
Project status: SIGNED

Accurate, detailed and high-frequent bathymetry, coupled with the important visual and semantic information, is crucial for the undermapped shallow coastal areas being affected by intense climatological and anthropogenic pressures. Regular UAV and satellite imagery have the potential to frequently and consistently map those areas to different extents and detail, providing ground breaking key information. However, optical properties of water severely affect images and refraction is the main factor affecting their geometry. Current Structure from Motion (SfM) based solutions for refraction correction are slow and costly. Satellite Derived Bathymetry (SDB) methods deliver faster results over huge shallow areas albeit in lower spatial resolution, failing to handle non-homogeneous seabeds. Recent methods based on Convolutional Neural Networks (CNNs) deliver either only the bathymetry or the semantics of the scene, tackling those problems separately and in one scale/modality at a time. They are mostly dedicated to satellite images, failing to address the challenges of shallow waters, being also inefficient for UAV images, preventing higher resolution results. MagicBathy will establish an advanced deep learning framework for low-cost shallow water mapping by developing a novel boundary-aware multitask, multiscale and multimodal learning approach for bathymetry and semantics together, exploiting single either UAV or satellite imagery. To overcome the domain gap, generalize and improve performance, self-supervised in-domain representation learning will be performed. To enhance the spatial resolution of low resolution satellite images and hence of the resulting bathymetric/semantic maps, a conditional generative adversarial network (cGAN)-based Super Resolution framework will be developed, dealing with the special challenges of shallow water imagery. Frameworks, models and results will be published in open access, enabling the rapid progress in shallow water mapping worldwide

Keywords: remote sensing,deep learning,seabed mapping,multitask learning,semantic segmentation,uav and satellite image,bathymetry,single view depth estimation,super resolution,representation learning
StrUcture PREserving Methods on Unstructured grids for continuum Mechanics (SUPREMUM)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Mathematics
Panel description: All areas of mathematics, pure and applied, plus mathematical foundations of computer science, mathematical physics and statistics
Countries involved: Spain
Call: ERC STARTING GRANTS (ERC-2025-STG, 2025)
Start date: 2026-02-01
End date: 2031-01-31
Duration (months): 60.0
Total project cost (EUR): 1500000.0
EU funding (EUR): 1500000.0
Participants: 1
Project status: SIGNED

SUPREMUM aims to develop and theoretically study innovative structure-preserving (SP) numerical methods to solve challenging non-linear hyperbolic partial differential equations (PDE) modelling continuum mechanics. The ultimate groundbreaking goal consists on devising the first existing numerical methods simultaneously verifying at the discrete level all structural properties of the continuous models: provable thermodynamical compatibility, so that the systems satisfy an extra conservation law for the total energy in order to rigorously prove non-linear stability; conservation of equilibrium solutions, needed to perform very long-time stable simulations; verification of natural involution constraints, of the divergence and curl type; and preservation of the asymptotic limits, arising for some characteristic scales tending to zero. We will devise efficient high order numerical schemes combining different families of numerical methods on unstructured staggered grids configurations, leading to the advance of fundamental mathematical knowledge on numerical analysis. The use of an arbitrary Eulerian-Lagrangian approach will allow the design of an efficient and accurate fluid structure interaction (FSI) solver based on the unified hyperbolic Godunov-Peshkov-Romenski (GPR) model of continuum mechanics able to represent both solids and fluids. Fundamental progress in practical applications of the developed methodologies will initially target the design of a monolithic approach for the simulation of entire cardiovascular systems, coupling 1D blood flow models to the 3D FSI approach. Finally, we will build the bridge between SP methods for hyperbolic PDEs and reduced order modelling (ROM) breaking the frontiers of present research to obtain a novel SP-ROM approach for conservation laws founded on a solid mathematical background. The investigations pursued will impact digital transformation opening new research avenues in numerical analysis, engineering and biomedicine.

Keywords: structure preserving numerical methods thermodynamical compatibility finite volume schemes continuous and discontinuous galerkin finite element schemes reduced order modelling unstructured grids hyperbolic partial differential equations fluid structure interaction godunov peshkov romenski model continuum mechanics