Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Reconciling Classical and Modern (Deep) Machine Learning for Real-World Applications (APHELEIA)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Computer Science and Informatics
Panel description: Informatics and information systems, computer science, scientific computing, intelligent systems
Countries involved: France
Call: ERC CONSOLIDATOR GRANTS (ERC-2022-COG, 2022)
Start date: 2023-09-01
End date: 2028-08-31
Duration (months): 60.0
Total project cost (EUR): 1999375.0
EU funding (EUR): 1999375.0
Participants: 1
Project status: SIGNED

Despite the undeniable success of machine learning in addressing a wide variety of technological and scientific challenges, the current trend of training predictive models with an evergrowing number of parameters from an evergrowing amount of data is not sustainable. These huge models, often engineered by large corporations benefiting from huge computational resources, typically require learning a billion or more of parameters. They have proven to be very effective in solving prediction tasks in computer vision, natural language processing, and computational biology, for example, but they mostly remain black boxes that are hard to interpret, computationally demanding, and not robust to small data perturbations. With a strong emphasis on visual modeling, the grand challenge of APHELEIA is to develop a new generation of machine learning models that are more robust, interpretable, and efficient, and do not require massive amounts of data to produce accurate predictions. To achieve this objective, we will foster new interactions between classical signal processing, statistics, optimization, and modern deep learning. Our goal is to reduce the need for massive data by enabling scientists and engineers to design trainable machine learning models that directly encode a priori knowledge of the task semantics and data formation process, while automatically prefering simple and stable solutions over complex ones. These models will be built on solid theoretical foundations with convergence and robustness guarantees, which are important to make real-life trustworthy predictions in the wild. We will implement these ideas in an open-source software toolbox readily applicable to visual recognition and inverse imaging problems, which will also handle other modalities. This will stimulate interdisciplinary collaborations, with the potential to be a game changer in the way scientists and engineers design machine learning problems.

Keywords: computer vision image processing optimization
Real-time nutrient sensing for mapping fertilizer needs (LiveSen-MAP)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Transition
Destination: Open to supporting projects in all areas of science, technology,...
Action type: EIC actions
Countries involved: Germany
Call: EIC Transition Open 2022 (HORIZON-EIC-2022-TRANSITION-01, 2022)
Start date: 2023-06-01
End date: 2026-05-31
Duration (months): 36.0
Total project cost (EUR): 2499955.0
EU funding (EUR): 2499955.0
Participants: 1
Project status: SIGNED

Within the project LiveSen-MAP, we will collect in collaboration with farmers for the first time a high-resolution dataset of the crop nutrient status over multiple growing seasons, to validate and deploy an ensemble of predictive models for field-scale and region-scale nutrient prescriptions. We will produce 20,000 of our test strips for the main plant nutrients N-P-K for field testing at test sites in Bavaria and North Rhine-Westphalia over the project duration. Together with farmers, we will develop our business model and drive product development. Collaborating with potential customers early on will be key for (i) deploying our technology to create the dataset needed to consolidate machine learning-based predictions and for (ii) testing the business model and verify our competitive position in the intended market. The insights gained from potential customers will be combined with a detailed stakeholder and market analysis to validate the business model and pricing strategy. We will be able to identify and engage key strategic partners such as agricultural and digital businesses. In this way, we can build and strengthen our network to highlight the unique value of our approach as a tool for reorienting towards a more sustainable and resource-efficient agriculture in Europe and beyond. The project results will be commercially exploited by a company that will over a subscription-based fertilization recommendation service to farmers.

Keywords: precision farming,artificial intelligence,fertilizer,point of use sensing,user involvement,big data
Plant pest prevention through technology-guided monitoring and site-specific control (PURPEST)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Food, Bioeconomy Natural Resources, Agriculture and Environment
Theme: Bio-Environment
Destination: Fair, healthy and environmentally-friendly food systems from primary production to consumption
Action type: Research and Innovation actions
Countries involved: Norway,Germany,Slovakia,Lithuania,France,Netherlands,Portugal,Switzerland,United Kingdom,Italy,Czechia
Call: Tackling outbreaks of plant pests (HORIZON-CL6-2021-FARM2FORK-01, 2021)
Start date: 2023-01-01
End date: 2026-12-31
Duration (months): 48.0
Total project cost (EUR): 6472232.5
EU funding (EUR): 6472231.75
Participants: 18
Project status: SIGNED

The EU requires rapid and effective actions based on innovative detection concepts targeting quarantine, priority and other serious pests. Fair, healthy and environment-friendly food systems are threatened by increasing pest invasions due to climate change and a growing demand for high quality, pest-free food. The goal of PurPest is to develop, validate and demonstrate an innovative sensor platform that can rapidly detect five different pests during import and in the field to stop their establishment and reduce pesticide inputs by at least 50%. The sensor concept is based on detection of pest-specific volatile organic compounds (VOCs) emitted by host plants invaded by one or several pests. PurPest will determine the VOC signature of Phytophthora ramorum, the Fall armyworm, the Cotton bollworm, the Brown marmorated stinkbug and the Pinewood nematode under different abiotic stress conditions. The VOC database will be exploited to optimize existing and develop new sensor concepts to detect pest-specific VOCs, starting from proof of concept (TRL3) to demonstration in field trials (TRL6). Non-invasive, reliable and rapid pest sensing platforms will increase pest screening efficiency from currently 3% to 80% of plant imports. Preventing outbreaks of new pests and site-specific pesticide use by early detection are the cornerstones of sustainable and integrated pest management (IPM). PurPest will evaluate the socio-economic and ecological impact of 5 pests and how the new detection concept affects these impacts. Direct communication with stakeholders via the advisory board, workshops and webinars is part of PurPest’s multi-actor approach to affirm involvement of all interest groups along the value chain The PurPest project is a strong multidisciplinary consortium with expertise from 10 countries, 7 universities, 5 research institutes, 4 SMEs and 2 governmental agencies.

Keywords: sensors,pest detection,monitoring,volatile organic compounds,odours,import control
Gene Environment interactions in Mental health trajectories of Youth (Youth-GEMs)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Health
Theme: Health
Destination: Staying Healthy in a rapidly changing society
Action type: Research and Innovation actions
Countries involved: Netherlands,Ireland,Germany,Serbia,Italy,Croatia,Spain,Australia,United Kingdom,Estonia,Finland
Call: Towards a molecular and neurobiological understanding of mental health and mental illness for the benefit of citizens and patients (HORIZON-HLTH-2021-STAYHLTH-01, 2021)
Start date: 2022-06-01
End date: 2027-05-31
Duration (months): 60.0
Total project cost (EUR): 8121855.0
EU funding (EUR): 8107980.0
Participants: 19
Project status: SIGNED

Youth mental health is heavily burdened, with life-long enduring impact on European citizens and societies. Trajectories of mental health and illness in young people are assumed to be determined by interplay between genetic, epigenetic, and environmental risk impacting during development. However, direct evidence for this is sparse and scientific progress is challenged. We recently initiated substantial advances enabling us to create necessary breakthroughs at the most pressing needs and challenges. Aiming to significantly reduce mental suffering and illness among European youth within the next 5-10 years, we will provide 1) the world?s first, evidence-based knowledge base of functional (epi)genomics of the developing post-natal human brain in direct relation to developmental trajectories of trans-syndromal phenotypes of mental illness, providing improved risk markers and actionable biological targets, 2) reliable predictive models, while identifying gene-environment interplay, as well as actionable markers of trajectories of mental (ill)health in young people through the use of Artificial Intelligence (AI)-based and inference-based analyses of unprecedented sets of longitudinal general population datasets, 3) the first comprehensive, validated set of evidence-based behavioural, environmental, biological, and psychological-informed instruments for the robust quantitative clinical assessment of mental health for help-seeking young people aged 12-24 years, harmonised across European clinical settings, and 4) youth- and clinician-empowering AI-driven instruments for early (self)detection, prediction and monitoring of mental ill-health trajectories in youth. Our multidisciplinary consortium is uniquely equipped and positioned to enforce the necessary breakthroughs for significant reduction of mental illness and suffering of young people, and to translate our findings into clinical innovation and life-long impact in Europe and beyond.

Keywords: youth mental health,gene environment interactions,epigenetic,development,child and adolescent psychiatry,complex genetics,machine learning,brain
Coded Secrets: Artistic Interventions Hidden in the Digital Fabric (COSE)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Texts and Concepts
Panel description: Literary studies , literature,  philosophy
Countries involved: Germany
Call: ERC CONSOLIDATOR GRANTS (ERC-2021-COG, 2021)
Start date: 2022-09-01
End date: 2027-08-31
Duration (months): 60.0
Total project cost (EUR): 1997348.75
EU funding (EUR): 1997348.75
Participants: 1
Project status: SIGNED

What lies beneath the surface of computational artworks? Online pieces have ‘roots’ that extend much deeper than the flat screen monitors on which we view them – they may even be distributed, occupying multiple sites on the Internet. Nobody has ever been able to see the responsive and connective agency of these works with their own eyes. COSE sets out to reveal the inner workings of code-based artworks, as well as their embeddedness in the various niches of the World Wide Web. Shedding light on this black box will enable us to gain a fuller appreciation of these artworks and empower scholars in the humanities as they begin to confront programmed works, providing analytical instruments and a general understanding of our media-technological condition. The artistic pieces under analysis are all hidden, concealed, or somehow withdrawn due to the networked situation into which they were inserted. As art historians prefer to dedicate themselves to the surface features, they tend to overlook such works or ignore important aspects of their design: codes, files, software performance. As these interventions operate in non-standard locations, they implicitly highlight the circumstances where artists saw opportunities to critically exploit the specifics of the net in order to post a message. COSE will offer a new view of the Internet through the lens of these artworks that reclaim the right to productively diversify Internet access and usage. In order to uncover these much-neglected aspects, an interdisciplinary team will complement art historical methods with approaches from media, game and code studies, software forensics and visual design. Taking the artworks as the starting point, the Internet will be presented as complex of activated affordances rather than as yet another node-graph-diagram. COSE will develop an original pictorial language to generate immersive views into the specific ‘machine rooms’ of the artworks and their ecosystems as a processual deep topology.

Keywords: net art,digital humanities,new media art,
New methodologies for automated modeling of the dynamic behavior of large biological networks (AUTOMATHIC)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Products and Processes Engineering
Panel description: Product and process design, chemical, civil, environmental, mechanical, vehicle engineering, energy processes and relevant computational methods
Countries involved: Netherlands
Call: ERC STARTING GRANTS (ERC-2024-STG, 2024)
Start date: 2024-12-01
End date: 2029-11-30
Duration (months): 60.0
Total project cost (EUR): 1500000.0
EU funding (EUR): 1500000.0
Participants: 1
Project status: SIGNED

In silico models aim to capture and elucidate the complex and emergent interactions of biological systems, with the goal of expediting research and potential clinical translation. For example, ordinary differential equation (ODE) models of toxin and drug transport are being developed to bring safer therapies to chronic kidney disease patients. Despite recent progress, these cutting-edge ODEs only model transport in steady state and remain limited regarding the amount and complexity of dynamic transport mechanisms as it is often not clear which kinetic relation is most suitable. This limitation is due to the manual and labor-intensive approaches to construct the ODEs, which critically hinder their application in quantitative toxicity assessment in key industrial settings like drug development. In AUTOMATHIC, I will leverage and combine my expertise in mathematical modeling and machine learning to develop an integrated framework for automated ODE structure identification, parameter estimation and model evaluation, focusing on cell transport and signaling, which is the timely leap forward needed to create large dynamic models and transform the field. I will test and illustrate the capabilities of the developed framework by exploring the dynamics and regulation of proximal tubule (PT) toxin and drug transport. I will use the dynamic PT model to define novel therapeutic regimens that minimize toxin accumulation in combination with a state-of-the-art in vitro set-up to measure the essential time series data required for model calibration and validation. The anticipated outcomes of AUTOMATHIC are: 1) a next-generation, integrated framework for automated model structure identification and parameter estimation that will become the new standard for the creation and interrogation of large dynamic models of cell transport and signaling; 2) crucial knowledge about PT transport and metabolism; 3) a dynamic PT model to optimize nephrotoxicity protocols, drug dosing and screening.

Keywords: computational modelling automated model identification ordinary differential equations proximal tubule drug toxin interaction
FORENSIC METHYLATION ANALYSIS TOOLSETS (FORMAT) (ForMAT)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Civil Security for Society
Theme: Civil Security for Society
Destination: Fighting Crime and Terrorism
Action type: Research and Innovation actions
Countries involved: Spain,United Kingdom,Portugal,Austria,Netherlands,Poland,Sweden,Germany
Call: Open Topic (HORIZON-CL3-2024-FCT-01, 2024)
Start date: 2025-09-01
End date: 2029-08-31
Duration (months): 48.0
Total project cost (EUR): 4673456.25
EU funding (EUR): 4673455.25
Participants: 11
Project status: SIGNED

The ForMAT project addresses limitations in forensic DNA profiling, particularly in criminal cases, disaster victim identification (DVI), and legal age assessment for asylum claimants. Traditional DNA methods struggle with unsolved cases, mass disasters, and legal age estimation. ForMAT focuses on DNA methylation (DNAm) as a solution, using epigenetic markers to estimate age and lifestyles, as well to predict tissue type. This approach enhances criminal investigations by narrowing suspect profiles and expedites missing person identification in mass disasters, improving the return of remains to families. In asylum cases, DNAm offers a radiation-free alternative for assessing age, reducing reliance on X-rays. ForMAT builds on the VISAGE project, aiming to advance these tools from Technology Readiness Level (TRL) 5 to TRL 7. By developing new DNAm kits and a bioinformatic applicaiton including forensic prediction models, ForMAT enables law enforcement to estimate age, to infer lifestyles and to identify the tissue-of origin from DNA samples. Kits include a DNAm somatic kit, as well as a DNAm germ-line kit for criminal cases, a DNAm DVI kit for guiding in the identification process of human remains, and a DNAm legal age kit for asylum assessments. The integration of Next Generation Sequencing (NGS) technologies, including second- and third-generation sequencing, enhances forensic workflows and ensures high accuracy even with degraded samples. The ForMAT consortium , consisting of 11 EU institutions, leverages expertise across countries and disciplines to bring these innovations to real-world use. Members include leading universities, police bodies, and forensic institutes, committed to scaling up, testing, and validating these tools across Europe. Their involvement in international forensic boards ensures widespread dissemination, aiming for broad adoption of ForMAT’s epigenetic molecular tools in forensic science, ultimately enhancing investigative practices and public safety.

Keywords: epigenetics,dna methylation,criminal cases,identification of missing persons,legal age
New & improved EUSST Missions and Services (HE23-24 TOP1)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Digital, Industry and Space
Theme: Cluster 4 other
Destination: Other Digital, Industry and Space destinations
Action type: Research and Innovation actions
Countries involved: Spain,France,Poland,Italy,Germany
Call: New & improved EUSST Missions and Services (HORIZON-CL4-2025-SSA-SST-IBA, 2025)
Start date: 2026-01-01
End date: 2028-06-30
Duration (months): 30.0
Total project cost (EUR): 6000000.0
EU funding (EUR): 6000000.0
Participants: 6
Project status: SIGNED

This Horizon Europe grant is to be understood within a wider context on EUSST, where other grants are being executed simultaneously with the common objective of improving the EU autonomy on Space Surveillance and Tracking. The specific objectives of the HE_EUSST_MS_TOP1 action have been defined considering the specific objectives defined in the Work Programme: * Keep the knowledge and capabilities of Europe on the Space Surveillance and Tracking domain at the leading edge. * Adapt, improve and evolve the current EUSST initial services (Collision Avoidance; Fragmentation; Re-entry) portfolio to future user needs and space environment. * Improve the overall performance of the EUSST services and ensure, in the long-term, a high level of performance and appropriate autonomy at Union level. * Identify and define new missions and services (e.g. debris mitigation; debris remediation). * Explore the implementation of new services, in complement of the three existing ones. * Support the pre-developments and end-to-end early demonstration of new SST services. The state of the art on the topic can be summarized as the output of the finished 2-3SST2018-20 and the 6 grants currently ongoing, in particular, EUSST2023-25 and HE TOP1 corresponding to Work Programme 21-22.

Keywords: sst,eusst,eu,services,partnership,catalogue,collision avoidance,re entry,fragmentation,survey,tracking
Enhanced selectivity VOC detection using novel GC-QEPAS (EVOQUE)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Digital, Industry and Space
Theme: Digital
Destination: Digital & Emerging Technologies for Competitiveness and Fit for the Green Deal
Action type: Innovation actions
Partnership: Photonics (co-programmed)
Countries involved: Ireland,Italy,France
Call: Advanced imaging and sensing technologies (IA)(Photonics Partnership) (HORIZON-CL4-2023-DIGITAL-EMERGING-01, 2023)
Start date: 2024-01-01
End date: 2027-06-30
Duration (months): 42.0
Total project cost (EUR): 5354072.15
EU funding (EUR): 4539801.13
Participants: 11
Project status: SIGNED

Volatile organic compounds (VOCs) are organic chemicals that have a high vapor pressure at room temperature. Based on their origin and formation mechanisms, VOCs can be markers in many industrial processes; in food production as quality markers, technological indicators, process contaminants; in plant phenotyping as botanical/geographical tracers, authenticity markers; and in crops storage as shelf-life indicators. VOCs also pose a range of hazards to human health and the environment. EVOQUE’s main objective is to develop a novel photonic-based sensory system with potential to outperform the current standards and to meet the challenging requirements of at-line, on-line & in-field needs of Agriculture, Food, Environmental Pollution monitoring and Industrial Emissions monitoring. EVOQUE will combine gas chromatography (GC), Quartz Enhanced Photo-Acoustic Spectroscopy (QEPAS), long wavelength quantum cascade lasers (QCLs) and mid-IR metasurfaces to produce a compact, easy to use, low cost, at-line and ultimately online, field deployable VOCs analyser that will have equivalent performance to the gold standard lab based GC-MS. EVOQUE will leverage GC’s separation of components from complex mixtures, and QEPAS’s high sensitivity, specificity and quantification. Unlike simpler GC detectors, QEPAS will provide molecular recognition based on the specific spectral features of single VOCs. The system measures the target compound even in presence of interferents, thus avoiding issues with poor repeatability of elution times and reducing the false positive rate relative to GC with non-specific detectors. Also, optical spectroscopy of many VOCs is hampered by absorption by atmospheric compounds at the wavelengths of interest or overlapping absorption features of related molecules; using the GC column, these compounds are separated and eluted from the column at different times, thus unleashing the potential of photonics for sensitive fast, non-destructive measurements of VOCs.

Keywords: laser absorption spectroscopy quartz enhanced photoacoustic spectroscopy food quality monitoring gas chromatography quantum cascade lasers interband cascade lasers meta surfaces