Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Superimposing Model-Based Multisensory Touch Sensations on Everyday Surfaces (SuperTouch)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Systems and Communication Engineering
Panel description: Electrical, electronic, communication, optical and systems engineering
Countries involved: Netherlands
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): 1499998.0
EU funding (EUR): 1499998.0
Participants: 1
Project status: SIGNED

Imagine running your fingers over a hard, rough, cool brick wall yet feeling a soft, smooth, warm surface like fur. Recent advancements in wearable haptic technologies are opening new possibilities for augmenting natural fingertip sensations with artificial tactile feedback. However, systematically overlaying surfaces—each evoking distinct sensations like roughness, softness, or warmth through stimulating the skin with a combination of vibration, pressure, and temperature signals—with artificial sensations remains a significant challenge. This complexity stems from our limited understanding of synthesizing multisensory tactile experiences with specific perceptual attributes and superimposing these artificial sensations with natural ones perceived at the fingertip during active exploration. In SuperTouch, I aim to understand how to replace the tactile feel of any surface experienced through bare fingertips with new, desired sensations across the full spectrum of perceptual dimensions. I will achieve this goal through a model-based approach by applying precisely modeled multisensory stimuli—vibration, temperature, and pressure—to the finger phalanx. I will begin by identifying perceptually relevant tactile features from the finger-surface interaction data in my lab's recent database. These features will be the foundation for synthesizing artificial stimuli, rendered using the multisensory haptic surface and ring newly developed in my lab. By conducting psychophysical experiments and employing interpretable machine learning techniques, I will mathematically model a) the relationship between tactile features and the resulting sensory experiences and b) the integration of stimuli across multiple contact points, allowing for the precise generation of sensations. SuperTouch will pioneer a new frontier in haptics by introducing the first universal model-based superimposition approach for multisensory cutaneous sensations, advancing the science of touch.

Keywords: haptics,human tactile perception,tactile data processing,multisensory cutaneous processing
Dissecting the molecular mechanisms underlying hnRNP exclusion from DNA double-strand breaks. (hnRNPs-DNA damage)

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: Life Sciences
Countries involved: Germany
Call: MSCA Postdoctoral Fellowships 2023 (HORIZON-MSCA-2023-PF-01, 2023)
Start date: 2024-04-01
End date: 2026-03-31
Duration (months): 24.0
Total project cost (EUR): 173847.36
EU funding (EUR): 173847.36
Participants: 1
Project status: SIGNED

DNA double-strand breaks (DSBs) are toxic lesions that must be efficiently repaired to maintain genome integrity. Following DNA damage, cells activate a multi-layered signalling and repair network termed the DSB response. Emerging organizers of this DSB response are various RNAs and RNA-binding proteins, which display characteristic localization dynamics at the damaged-chromatin. The laboratory of Stephanie Panier recently showed that many heterogeneous nuclear ribonucleoproteins (hnRNPs) are actively excluded from damaged chromatin soon after the induction of DSBs. The exact purposes of hnRNP exclusion and the underlying regulatory mechanisms remain to be determined. In my postdoctoral project, I propose to study why and how hnRNPs are excluded from damaged chromatin. To this end, 1) I will characterize the exclusion properties of the hnRNPs at DSBs using cellular models and unique genetic and microscopy approaches. 2) I will also identify protein and RNA complexes associated with hnRNPs at DSB sites using proximity labelling proteomics and RNA-sequencing approaches. In studying these hnRNPs, I will unravel the new mechanisms by which RNAs help to organize the DSB response. The combination of state-of-the-art cell biological and omic approaches will enable me to go well beyond the current knowledge of the field to characterize novel factors and mechanisms that coordinate RNA biology at DSB sites. Since genome instability and defects in RNA metabolism are both directly linked to the pathogenesis of cancer and also neurodegeneration, my findings will have direct implications for human health.

Keywords: genetic instability,dna double strand break response,hnrnps,rna,rna binding proteins
PAVEment SCANning with EGNSS Technology for Accurate Assessment (PAVE-SCAN)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Digital, Industry and Space
Theme: EU Space
Destination: EU Space
Action type: Innovation actions
Countries involved: Cyprus,Greece,Italy,Malta,Spain,Estonia,Austria
Call: EGNSS applications for Smart mobility (HORIZON-EUSPA-2022-SPACE, 2022)
Start date: 2024-01-01
End date: 2026-12-31
Duration (months): 36.0
Total project cost (EUR): 2971875.0
EU funding (EUR): 2418300.0
Participants: 13
Project status: SIGNED

The proposed project aims for the development to market (TRL8-9) of EGNSS-based integrated low-cost sensor technologies and artificial-intelligence-driven open-architecture software solution (machine learning (ML) and machine vision (MV)), for the detection, classification, and georeferencing of roadway pavement surface anomalies and for the low-cost assessment of roadway pavements using participatory sensing. The proposed system is of practical importance since it provides continuous information about roadway pavement surface anomalies which are valuable for efficiently monitoring the transport infrastructure and for public safety. The vision for roadway condition assessment by utilization of smartphone-like technology is set in parallel with the hypothesis that such technology can be used for crowd-sourced data collection and analysis in GIS-based pavement management systems (PMS), and that the developed technology and related transport informatics are disruptive technologies that have the potential to reshape the transport and infrastructure O&M industries through the project objectives discussed in the proposal.

Keywords: egnss sensors,transport infrastructure management,roadway pavements,anomaly detection,augmented reality
Convolutional neural networks to reveal resistant phenotypes behind the complex genotypes of ovarian cancer (CONVO)

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: Life Sciences
Countries involved: Finland
Call: MSCA Postdoctoral Fellowships 2021 (HORIZON-MSCA-2021-PF-01, 2021)
Start date: 2022-05-01
End date: 2024-04-30
Duration (months): 24.0
Total project cost (EUR): 215534.4
EU funding (EUR): 215534.4
Participants: 1
Project status: CLOSED

High-grade serous ovarian cancer (HGSOC), which is the most aggressive type of ovarian cancer, is characterized by the mutation of gene TP53 and extensive copy number variations (CNVs). HGSOC tumors typically show an initial favourable response to standard treatments, however they often acquire resistance and relapse. Currently, there is a need for new therapeutic approaches to combat emerging drug-resistant subpopulations. Although, the scarcity of common targetable oncogenic mutations has complicated the development of directed therapies, the study of CNVs offers a promising opportunity to find new mechanisms of resistance and develop alternative treatments, as it has been described how CNVs can model clonal fitness and therapeutic resistance in other types of cancer. Here, I will explore the impact of the CNVs on the treatment response of HGSOC patients and their distal effects on the transcriptome using convolutional neural networks. This complex machine learning model will be trained with the largest available longitudinal cohort of HGSOC samples at the single-cell resolution and will reveal which CNVs, and their specific combinations, have a relevant role in shaping the HGSOC tumours upon treatment. Employing a systems biology approach I will identify convergent phenotypes within these relevant CNV profiles and then validate their effects on treatment using data from both external cohorts and drug-treated patient derived organoid models. This novel approach enables revealing resistance mechanisms driven by complex genotypes, and thus allows finding specific vulnerabilities to combat emerging resistance in HGSOC.

Keywords: copy number variation,ovarian cancer,single cell transcriptomics,machine learning
Rare decays of heavy mesons to resonant channels (RAD TORCH)

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: Physics
Countries involved: Switzerland
Call: MSCA Postdoctoral Fellowships 2022 (HORIZON-MSCA-2022-PF-01, 2022)
Start date: 2023-11-01
End date: 2025-10-31
Duration (months): 24.0
Total project cost (EUR): 210789.12
EU funding (EUR): 210789.12
Participants: 1
Project status: SIGNED

Reliable and precise Standard Model predictions are becoming increasingly important in searches for new physics as the gap closes between experiment and theory. At CERN I will perform the first physical-quark-mass calculation of resonant form factors, including B → K*. This will allow future analyses of the LHCb data set to give significantly stronger probes of flavour non-universality and pave the way to discover new physics from the current 2.5σ tension in the R(K*) ratio of branching fractions observed at LHCb. Access to those form factors can be gained by studying rare decay processes like B→K* ℓ+ ℓ− . These processes, which involve neutral-current b→s quark transitions, are forbidden at tree level in the Standard Model. The resonant final state poses a particular challenge on the lattice due to its Euclidean formalism, which requires a careful study of the finite volume spectrum which can be related to pion-kaon asymptotic scattering states. The computational strategy is the distillation technique, which projects computationally expensive propagators into a subspace to be re-used efficiently. I have written high-performance-computing (HPC) code for distillation as part of the lattice QCD softwares Grid and Hadrons. This is free, open-source and runs in data production on various computing architectures, including GPU-based ones. The other main challenge of the computation of B → K* form factors is the treatment of the heavy-quark on the lattice. My work on B-meson mixing has demonstrated the viability of using a fully relativistic action for the heavy quark with fully controlled systematic errors. The combination of the careful treatment of the resonant effects and the innovative approach to incorporate the heavy quark will be essential to advance the precision physics goals in the B-sector, as well as provide the platform to study other related processes like Kγ → Kπ from QCD.

Keywords: lattice qcd,resonances,spectroscopy,b physics
Theory of Fair Machine Learning (FairML)

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: Denmark
Call: MSCA Postdoctoral Fellowships 2022 (HORIZON-MSCA-2022-PF-01, 2022)
Start date: 2024-08-01
End date: 2026-07-31
Duration (months): 24.0
Total project cost (EUR): 214934.4
EU funding (EUR): 214934.4
Participants: 1
Project status: SIGNED

Designing fair machine learning algorithms is challenging because the training data is often imbalanced and reflects (sometimes subconscious) biases of human annotators, leading to a possible propagation of biases into future decision-making. Besides, enforcing fairness usually leads to an inevitable deterioration of accuracy due to restrictions on the space of classifiers. In this project, I will address this challenge by developing oracle bounds of fairness restraints and a Pareto-dominated trade-off between fairness and accuracy using ensemble classifiers with the majority vote, to cancel out not only errors but also biases. I will also develop illegal bias tracing and long-term fairness capturing to comply with anti-subordination lawfully, using learning theory tools including causality and online learning for moral responsibility. The central objective of this proposal is to gain a theoretical understanding of fairness and to design machine learning algorithms that simultaneously improve both fairness and accuracy. The study is essential both for improved scientific understanding of fairness in machine learning models, and for the development of fairer algorithms for the numerous application domains, such as recruitment, criminal judging, or lending. Moreover, the project also takes interdisciplinary knowledge of economics and law into account to avoid fairness concepts in machine learning from being misaligned with their legal counterparts, enlarging the impact of machine learning applications and giving back to the wider community.

Keywords: fairness,machine learning,majority vote,causality,multi armed bandits
SKIN-like TWO-Dimensional materials-based elecTRONICS conformable to rough surfaces (SKIN2DTRONICS)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Synergy grants
Action type: ERC actions
Countries involved: Italy,Spain,Switzerland
Call: ERC SYNERGY GRANTS (ERC-2024-SyG, 2024)
Start date: 2025-05-01
End date: 2031-04-30
Duration (months): 72.0
Total project cost (EUR): 9896897.0
EU funding (EUR): 9896897.0
Participants: 4
Project status: SIGNED

The goal of SKIN2DTRONICS is to demonstrate the large scale integration (LSI, transistor count larger than 1000) of soft and thin (skin-like) electronic devices on ultra-flexible substrates, capable of conformally adapting to any rough and curved surface. This vision will be realized by atomically thin two-dimensional materials (2DMs) that possess compelling properties for this application: high electronic performance, environmental stability, low toxicity and cytoxicity, and extreme resilience to mechanical deformations. With the increasing pressure towards ubiquitous electronics (wearables, Internet-of-Things, smart patches, etc.) it is urgent to develop electronics that can be easily integrated on the surface of everyday objects and, in the case of health monitoring applications, on a variety of rough biological surfaces. Today’s conformal electronics is mainly based on conformal sensors with flexible and stretchable electrodes interfaced to bulky silicon chips, responsible for processing. This approach is prone to mechanical failures, especially at the solderings, as the connection between the conformal and solid components remains very challenging. The research will rely on the complementary conjuncture of four fields: sensors, 2D-based electronics, flexible electronics and biomedical engineering that the PIs of the consortium bring, each of them recognized experts in their respective fields and with a valuable experience in leading ERC projects.

Keywords: conformable electronics,wearable systems,on skin electronics,ultra flexible circuits,two dimensional materials
Creating water-smart landscapes (WaterSmartLand)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Human Mobility, Environment, and Space
Panel description: Human geography, demography, health, susta inability science, territorial planning, spatial analysis
Countries involved: Estonia
Call: ERC CONSOLIDATOR GRANTS (ERC-2023-COG, 2023)
Start date: 2024-03-01
End date: 2029-02-28
Duration (months): 60.0
Total project cost (EUR): 1909500.0
EU funding (EUR): 1909500.0
Participants: 1
Project status: SIGNED

With the growing human population, the diffuse nutrient emissions from agriculture are expected to increase with the rise of fertilizer use. This situation has created a need for “sustainable intensification” by increasing yields while simultaneously decreasing the environmental impacts. Nature-based solutions (NbS) such as wetlands and riparian buffer strips can efficiently reduce the nutrient runoff from agricultural catchments. However, most land and water management studies mostly do not identify specific priority areas where the nutrient runoff to the water bodies is the highest (hotspots) nor do they provide spatially explicit solutions to improve the environmental conditions. Identification of priority areas will be important for ensuring cost-effective interventions to reduce the impact of intensive agriculture. The aim of the proposed project is to develop an analysis, modelling, and machine learning (ML) framework for finding spatially optimal land management scenarios for implementing NbS such as wetlands and riparian buffer strips to reduce agricultural nutrient runoff from catchments at different scales. Moreover, the project will identify the landscape predictor variables at different spatial scales for nutrient concentrations and their cross-scale interactions using ML. We will implement a novel Discrete Global Grid System data cube to manage all environmental data needed for modelling. We will take advantage of the strength and flexibility of existing ML methods to deal with complex ecosystem responses, and to reveal new interactions among water quality predictor variables. ML together with geospatial analysis will help us to develop different spatially explicit NbS allocation scenarios which we will evaluate with process-based hydrological modelling. In addition, we will address the challenges of processing large datasets by using proven parallelisation and distributed computing toolkits.

Keywords: water quality,sustainable agriculture,machine learning,hydrological modelling,geospatial analysis,data cube,discrete global grid system
Moonlighting: understanding the quality and consequences of working multiple jobs (MOONLIGHT)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: The Soci al World and Its Interactions
Panel description: Sociology, social psychology, education  sciences , communication  studies
Countries involved: Netherlands
Call: ERC STARTING GRANTS (ERC-2023-STG, 2023)
Start date: 2024-05-01
End date: 2029-04-30
Duration (months): 60.0
Total project cost (EUR): 2094859.0
EU funding (EUR): 2094859.0
Participants: 1
Project status: SIGNED

MOONLIGHT is a transdisciplinary and multi-method study of the quality of work and wellbeing of contemporary multiple jobholders. Working in the new economy is increasingly of a flexible and fragmented nature. Moonlighters, or multiple jobholders [MJHs], can be considered emblematic of how work fragmentation and combinations affect the quality of work and total worker wellbeing. MOONLIGHT will research the quality of work in first, second and other jobs and how this translates into total worker wellbeing. I introduce this concept of ‘total worker wellbeing’ to refer to the complex interdependencies and co-existence of features. This is both conceptually and empirically ground-breaking, as conceptual research and the development of comprehensive scales on wellbeing at work is still in its infancy and its inclusion enables researchers to make meaningful comparisons between groups of workers – both single and multiple jobholders. Apart from this conceptually innovative part, a unique and crucial feature of this programme’s empirical approach is its innovative app for dynamic quality and wellbeing assessment. MOONLIGHT will study the quality and wellbeing of MJHs through comparative research in seven advanced economies: Denmark, Germany, Italy, the Netherlands, Poland, the United Kingdom and the United States. Even though the meso- and macro-level contexts both have a pivotal role in the prevalence, quality and wellbeing of MJHs, these levels have so far rarely featured in research – a void this programme aims to fill. All countries have increasing levels of MJHs, but with different welfare state traditions, regulations and policies, norms and labour markets. By researching the practices used to conceptualise, classify and measure quality of work and wellbeing and translating these into a future-proof conceptual framework and app, that capture the complexities of the modern fragmented world of work, MOONLIGHT will provide both theoretical and methodological advancement.