Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Elucidating the bidirectional energy cascade of geophysical turbulence in time, space, and scale (GeoCascade)

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: Spain,Ireland
Call: MSCA Postdoctoral Fellowships 2022 (HORIZON-MSCA-2022-PF-01, 2022)
Start date: 2023-09-18
End date: 2025-09-17
Duration (months): 24.0
Total project cost (EUR): 181152.96
EU funding (EUR): 181152.96
Participants: 2
Project status: SIGNED

Turbulent fluid motions are responsible for closing the energy budget in Earth’s atmosphere and on many astrophysical bodies, dictating their long-term evolution and climate. However, geophysical turbulence, even in the simplest contexts, remains an open problem. Past work has shown that the theory for homogeneous and isotropic turbulence (HIT) breaks down in a fluid subject to rotation, stratification, or large aspect ratios. Particularly affected is the central insight from the study of HIT, stating that energy moves to smaller scales through an ‘energy cascade’. In geophysical flows, energy can flow both to larger and smaller scales through a ‘bidirectional’ cascade. The fraction of energy going to large scales depends on the value of the relevant geophysical parameter, becoming nonzero at an apparent critical point. The goal of this project is to identify the spatial and spectral signatures of the bidirectional cascade, understand their role in the cascade’s sudden onset, and develop a quantitative theory for its subsequent development. We plan to use a combination of numerical and statistical methods to explore the bidirectional cascade in space, time, and scale. This analysis will be done through two complementary perspectives, investigating turbulent structures in physical space and in spectral space. In the former, the turbulent cascade manifests itself as individual structures which break up, merge, or clump together, depending on the regime. A statistical view of these interactions will provide insight on how the nature of the flow changes. On the other hand, in spectral space, the phases of the complex velocity amplitudes are known to be responsible for the exchange of energy among different length scales. We will look into a possible partial synchronization between these phases in our simulations, and attempt to model the transition to a bidirectional cascade using tools from the rich field of synchronization in complex networks.

Keywords: turbulence,geophysical fluid dynamics
Hybrid Intelligence to monitor, promote and analyse transformations in good democracy practices (HYBRIDS)

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: Social Sciences and Humanities
Countries involved: Spain,Portugal,Germany,Italy,United Kingdom,Netherlands,France
Call: MSCA Doctoral Networks 2021 (HORIZON-MSCA-2021-DN-01, 2021)
Start date: 2023-01-01
End date: 2026-12-31
Duration (months): 48.0
Total project cost (EUR): 2695754.06
EU funding (EUR): 2587766.4
Participants: 14
Project status: SIGNED

False rumors, fake news or hate speech against vulnerable minorities through social media are becoming one of the main threats against democracies. A global strategy against disinformation is crucial as open democratic societies rely on free citizens with the ability to access verifiable information so that they can build their own opinions on different political issues. The main scientific objective of HYBRIDS is to provide researchers with the knowledge necessary to design strategies and tools to response to disinformation on the basis of a deep analysis of public discourse. There have been interesting advances in the automatic detection of disinformation by making use of natural language processing and new artificial intelligence techniques in the specific field of machine and deep learning. However, this is a very complex task that requires a high degree of natural language understanding, inference and reasoning. To improve the strategies to deal with disinformation, HYBRIDS will integrate the structured knowledge provided by social and human sciences into natural language processing tools and deep learning algorithms, so as to develop new hybrid intelligence systems. The concept of Hybrid Intelligence consists of combining machine and human intelligence to overcome the shortcomings of existing artificial intelligence methods. Even though hybrid systems are likely to become more and more decisive in the immediate future, there are very few experts who can design and develop such systems. This is mainly due to the multidisciplinary nature of the hybrid strategy and the difficulty of finding researchers with full training in traditionally separate disciplines such as computer engineering, social sciences or linguistics. We believe it is time to launch a DN with the necessary requirements to train researchers in hybrid methodologies for their application in social studies with an impact on the sustainability of good democratic practices in Europe.

Keywords: hybrid intelligence,natural language processing,artificial intelligence,discourse analysis,information extraction,disinformation,argumentation,political communication,social media platforms
Always-On Intelligent Sensing with the Spiking Neural Processor (Spiking Neural Processor)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Open to supporting projects in all areas of science, technology,...
Action type: EIC actions
Countries involved: Netherlands
Call: EIC Accelerator Open 2023 (HORIZON-EIC-2023-ACCELERATOR-01, 2023)
Start date: 2023-05-01
End date: 2025-04-30
Duration (months): 24.0
Total project cost (EUR): 3563250.0
EU funding (EUR): 2494275.0
Participants: 1
Project status: CLOSED

Over the last 5 years, the number of devices equipped with sensors has grown exponentially reaching 4 billion per year. In battery-powered devices such as phones, smartwatches, wireless earphones, & video doorbells, energy-efficient processing of the complex, continuous data coming out of multiple sensors is a critical problem. Innatera is a trailblazing semiconductor spin-off of the TU Delft & is bringing to the market neural processors. Our Spiking Neural Processor (SNP) is inspired by the way neurons function in our brain using a neural network with an inherent notion of time. At the sensor edge, SNP can deliver always-on pattern recognition functionalities in power & latency-sensitive applications via an innovative programmable architecture. It enables application-specific customers to implement immediately ready-to-use high-performance pattern recognition. SNP delivers market-leading differentiation with faster time-to-market & reduced development effort.

Keywords: neuromorphic processor,signal processing,time series processing,analog mixed signal computational fabric,neural networks,sensor,neural processor,accelerator,always on sensing applications,pattern recognition,spiking neural networks
Infusion of CRHR1 agonists in the lateral septum to rescue social preference in a mouse model of schizophrenia (SocialPref)

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: Spain
Call: ERC PROOF OF CONCEPT GRANTS (ERC-2024-POC, 2024)
Start date: 2025-07-01
End date: 2026-12-31
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: SIGNED

Patients affected with schizophrenia or social anxiety disorders such as separation anxiety or avoidant personality disorders often exhibit difficulties to socially interact, particularly with novel individuals. Altered social interactions impair their day-to-day life and often prevent them from having families or holding on to their jobs. In addition, the number of individuals living as recluses and suffering from the Hikkimori syndrome has sharply increased since the COVID pandemic. Currently, there is no dedicated medication to treat the crippling introversion associated with these disorders. We discovered a neuronal circuit regulating social preferences through the activation of the corticotropin-related hormone receptor 1 in the lateral septum (LS). Specifically, we demonstrated through a combination of electrophysiological, chemogenetic, optogenetic, calcium recording and gene silencing techniques that the release of CRH from prefrontal cortex (PFC) neurons into LS suppresses social interaction with familiar mice. This circuit therefore contributes to the social novelty preference exhibited by adult mice. based on these results, we presented a patent titled "CRHR1 agonists for use in the treatment of social anxiety disorders". In addition, preliminary data shows that social novelty preference as well as CRH signaling from the PFC to LS is dysregulated in the Df16(A)+/- mouse model of schizophrenia. In order to further interest for our patent from pharmaceutical companies, we would like to conduct a pre-clinical study testing whether infusion of CRHR1 agonists in the septum of Df16(A)+/- mice can rescue social novelty preference and improve social interactions in this model. Our overarching goal is to facilitate the development of new treatments for mental health disorder patients suffering from extreme introversion.
Non-invasive staining of tissue microstructure in temporal lobe epilepsy using in- vivo MRI (MRStain)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Prevention,  Diagnosis and T reatment of Human Diseases
Panel description: Medical technologies  and tools for prevention, diagnosis and treatment of human diseases, therapeutic approaches and interventions , pharmacology, preventative  medicine, epidemiology and public health , digital medicine
Countries involved: Germany
Call: ERC CONSOLIDATOR GRANTS (ERC-2022-COG, 2022)
Start date: 2024-07-01
End date: 2029-06-30
Duration (months): 60.0
Total project cost (EUR): 1994225.0
EU funding (EUR): 1994225.0
Participants: 3
Project status: SIGNED

Ex-vivo histology is the gold standard to investigate human brain microstructure. However, its invasive nature precludes its use in monitoring disease progression and the investigation of the pathophysiological origin of neurological disorders. MRStain will address this shortcoming by exploiting the sensitivity of the Magnetic Resonance Imaging (MRI) signal to estimate aggregated histological metrics in the human brain non-invasively. Like established histology staining methods (e.g., myelin-basic protein), MRStain will be sensitive to changes in cellular populations, axons, myelin, and iron. This will be achieved by augmenting the MRI measurements with computational biophysical models, which can disentangle tissue metrics at the micron-scale using the macroscopic spatial resolution (1–4 mm) of MRI. However, the clinical use of these models has not been employed because their validity and generalizability across disease trajectories has yet to be tested against the ex-vivo histological gold standard. This project will address this shortcoming by generating a globally unique multi-modal dataset that combines novel in-vivo and ex-vivo MRI techniques with biophysical models as well as cutting-edge large-scale 3D histology. The project will benefit from a unique translational university hospital environment where large sections of freshly excised brain tissue from drug-resistant temporal lobe epilepsy patients (TLE, 80 sections of about 3 x 2 x cm^3) can be examined, enabling in-vivo MRI-based biophysical tissue parameters to be validated against their histological gold standard. I will develop an MRStain model to identify TLE relevant changes that will achieve a paradigm shift in how epilepsy is treated by identifying target brain areas for surgery that will help to predict seizure-free outcome after surgery. The final validated MRStain models will also pave the way for similar noninvasive investigations of other neuropsychiatric diseases with unprecedented precision.

Keywords: mri,mr physics,biophysical models of mr signal,3d ex vivo histology in the human brain,physical informed models for artifact correction in mri,machine learning and cell segmentation,machine learning and mri
Strengthening Understanding and Strategies of business To Assess and Integrate Nature (SUSTAIN)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Food, Bioeconomy Natural Resources, Agriculture and Environment
Theme: Bio-Environment
Destination: Biodiversity and Ecosystem Services
Action type: Research and Innovation actions
Countries involved: United Kingdom,Netherlands,Belgium,Switzerland,Spain
Call: Impact and dependence of business on biodiversity (HORIZON-CL6-2021-BIODIV-01, 2021)
Start date: 2022-09-01
End date: 2026-02-28
Duration (months): 42.0
Total project cost (EUR): 1268251.23
EU funding (EUR): 1268251.23
Participants: 11
Project status: SIGNED

Our global economy is intrinsically dependent on nature, and at the same time is playing a major role in its degradation. Recognition of this fact has grown substantially in the past decade, as evidenced in the EU Biodiversity strategy for 2030. However, transformative, system-wide changes are still needed to achieve the nature-positive outcomes required to 'bend the curve' of biodiversity loss. While the business case for economic actors to address biodiversity is becoming clearer in some sectors, it is still lacking in others, particularly those more removed from direct interactions with nature. SUSTAIN (Strengthening Understanding and Strategies of business To Assess and Integrate Nature) will bring together a multi-stakeholder and multi-disciplinary team to strengthen understanding and awareness of how all economic activities depend and impact on biodiversity. The project will build on existing work within the business and biodiversity space to develop and validate a database of business dependencies and impacts, develop methods that actors can use to reduce biodiversity-related risks, and a toolbox to support their application. The consortium will drive uptake of these resources through dissemination of targeted business case materials, drawing on existing networks and expertise in communicating with businesses, financial institutions and other key stakeholders.

Keywords: biodiversity,ecosystem services,private sector,measurement,monitoring,reporting
High-throughput Discovery of Catalysts for the Hydrogen Economy through Machine Learning (HighHydrogenML)

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: Chemistry
Countries involved: Spain
Call: MSCA Postdoctoral Fellowships 2022 (HORIZON-MSCA-2022-PF-01, 2022)
Start date: 2023-04-01
End date: 2025-07-31
Duration (months): 28.0
Total project cost (EUR): 174222.96
EU funding (EUR): 174222.96
Participants: 1
Project status: CLOSED

Hydrogen energy storage offers a unique combination of scalability, long-term storage, and portability, leading to the so-called hydrogen economy. The major challenge in the hydrogen economy is related to the production of hydrogen from water and the generation of energy by the oxidation of hydrogen into water. In this regard, the main objective of the project High-throughput Discovery of Catalysts for the Hydrogen Economy through Machine Learning (HighHydrogenML) is to develop a high-throughput strategy based on first principles calculations and artificial intelligence tools to discover intermetallic compounds whose catalytic activity can be tuned to reach an optimum catalytic performance for the Hydrogen Evolution Reaction (HER) and Oxygen Reduction Reaction (ORR) by means of elastic strain engineering. The successful completion of these objectives will provide unique information for experimental synthesis of intermetallic compounds with high catalytic activity for the HER and ORR and could, therefore, open a new avenue for a feasible and efficient hydrogen economy. Moreover, the strategies and tools developed in this project can be applied later to many other catalytic processes of large industrial and/or environmental interest. To achieve these goals, the project HighHydrogenML involves multidisciplinary expertise in solid state physics, materials science, machine learning, and chemistry that will be coupled in a seamless framework to exploit the high predictive power of ab initio calculations in conjunction with the efficiency of ML models. Therefore, this project brings together a researcher with expertise in atomistic and materials modelling within a broad range of different computational chemistry methods and artificial intelligence techniques, a world-recognized supervisor in the area of multiscale modelling of materials, and a research institute with a record of excellence, technology transfer, and top-level training in Materials Science and Engineering.

Keywords: elastic strain engineering,high throughput screening,hydrogen evolution reaction,oxygen reduction reaction,intermetallic compounds,catalytic activity
Reliable Conversational Domain-specific Data Exploration and Analysis (ARMADA)

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: Information Science and Engineering
Countries involved: Italy,Denmark,Greece,France,Netherlands,Sweden,Austria,Switzerland
Call: MSCA Doctoral Networks 2023 (HORIZON-MSCA-2023-DN-01, 2023)
Start date: 2025-03-01
End date: 2029-02-28
Duration (months): 48.0
Total project cost (EUR): 3289140.0
EU funding (EUR): 3289140.0
Participants: 12
Project status: SIGNED

Conversational AI and Large Language Models (LLMs) such as ChatGPT and Bard promise to answers complex problems by performing simple conversations. Unfortunately, their answering processes are inscrutable, as well as prone to bias, hallucinations, and high computational costs. The ARMADA doctoral network will train 15 highly skilled Early Stage Researchers to specialize in the area of Conversational AI and the challenges associated to the recent advances in developing LLMs, when assisting analysis in sensitive domains. These specialists will acquire unique knowledge and skills in Natural Language Processing, Machine Learning, Data Management, and Algorithms to evaluate and improve the reliability of LLMs. A reliable LLM will produce timely, consistent, and verifiable answers, and provide guidance to the user in important decision-making processes. This will build across 5 important axes: alignment with domain knowledge, explainability and soundness of answers, reactivity via interactive correction workflows, and effectiveness and efficiency of computations. Due to the highly interdisciplinary aspect, the proposed program will ensure a number of training activities targeted to hone the skills of the trainees across different dimensions. The network provides research training with summer and winter schools on the multidisciplinary themes, as well as workshops and courses to foster complementary-skills, such as scientific writing, innovation, supervision, and management. This program importantly tackles the crucial EU needs for regulating AI by offering to train experts in the area of Conversational AI that will potentially advise EU bodies on technical matters related to the adoption of these technologies in critical disciplines, such as medicine, education, and business intelligence. The 8 organizations distributed in 7 countries and managed by a highly diverse team of expert researchers will form an interoperability scheme to share knowledge and skills.

Keywords: large language models,data analytics,interactive data analysis,decision support systems
stRengthening human capital and increasing the excellencE of Widening-country organisations iN R&I For bio-based, lOw-risk and smaRt Crop protection (REENFORCE)

Framework: Horizon Europe — Widening Participation and Strengthening the European Research Area
Programme: Widening participation and spreading excellence
Theme: Attracting and mobilising the best talents
Destination: Attracting and mobilising the best talents
Action type: Coordination and support actions
Countries involved: Greece,Belgium,Portugal
Call: ERA Talents (HORIZON-WIDERA-2024-TALENTS-03, 2024)
Start date: 2025-09-01
End date: 2029-08-31
Duration (months): 48.0
Total project cost (EUR): 2998048.75
EU funding (EUR): 2998048.75
Participants: 9
Project status: SIGNED

The REENFORCE project, led by the Agricultural University of Athens, aims to create a long-lasting cross-sectoral collaboration, to empower the R&I human capital base, increase the cross-sectoral collaboration and enhance the excellence of research performing organisations in the Widening countries of Greece and Portugal. The project’s thematic area lies in sustainable crop protection, more specifically in the topics of: (i) bio-based solutions and low-risk pesticides, (ii) ecotoxicology assessment of applied substances and (iii) smart spraying applications. REENFORCE addresses Action 4 of the ERA Policy Agenda 2022-2024, while it supports Europe's twin green and digital transition, in the context of agriculture and sustainable crop protection. REENFORCE consortium consists of 4 academic organisations, 4 private companies and 1 competence centre from two Widening countries (Greece and Portugal) and one non-Widening country (Belgium), specialising in the fields of bio-based, low-risk and smart application of pesticides. REENFORCE will map relevant needs and knowledge gaps in R&I and cross-sectoral collaborative research in Widening countries, defining the requirements for upskilling human capital and enhancing research organisations' excellence, following business sector demands. It will develop specialised training material, and will conduct multiple training activities (face-to-face & online), cross-border and cross-sectoral secondments of R&I talents (in the context of joint research studies and shared use of infrastructure), providing opportunities for personal career development. REENFORCE will propose best practices, institutional reforms and policy recommendations, to increase the excellence of R&I, and research support, in Widening countries, and will widely promote the economic, environmental and societal benefits of bio-based, low-risk and smart application of pesticides.

Keywords: sustainable crop protection,randi,human capital,secondments,trainings,career development,widening countries