Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Language contact and heterogeneity in the Hybrid Chinese dialects in North-West China (LACONC)

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: Social Sciences and Humanities
Countries involved: Italy
Call: MSCA Postdoctoral Fellowships 2023 (HORIZON-MSCA-2023-PF-01, 2023)
Start date: 2024-12-01
End date: 2026-11-30
Duration (months): 24.0
Total project cost (EUR): 172750.08
EU funding (EUR): 172750.08
Participants: 1
Project status: TERMINATED

This project focusses on highly endangered hybrid Chinese dialects spoken in the Gansu-Qinghai aera, namely Wutun, Zhoutun, Gangou, Xining, Linxia and Tangwang, which are reputed to have been highly “Altaicised”. The main objective is to determine their position within the North-Western branch by identifying the different strata of Altaic influence found on the morphonological level. It aims at demonstrating the high degree of heterogeneity of certain specific features that are reputed to have been induced by contact with Mongolic and Turkic languages, namely nominal case markers, pronominal forms (third person and demonstratives) , and tense and aspectual markers. As several Mongolic languages are spoken by different minorities in this region, many scholars thought that these non-Sinitic traits have been induced through contacts with these populations and that a linguistic area has formed, leading to their areal diffusion. However, many of those are also found in dialects that have not been in direct contact with Altaic languages in recent time, including those spoken along the Yellow river spread between the Shanxi and Shaanxi regions. Therefore, a certain continuity between the dialects of these 2 regions exists, while secondary influence of Mongolic languages has further developed the changes in those dialects. By comparing this selection of forms, the project will 1. Document the typological diversity found in the hybrid Chinese dialects of the Gansu-Qinghai area (suggesting different source of borrowing or a different reanalysis process) 2. Link the contact-induced traits and forms identified in the Gansu-Qinghai hybrid dialects with those of the dialects spoken in he Shaanxi-Shanxi region. 3. Identify the different strata of Altaic influence in the hybrid dialects 4. Show that the secondary contact-induced features have been transferred through direct contacts with Mongolic languages, and that several isoglosses can be identified rather than a linguistic area

Keywords: gansu qinghai dialects,language contact,hybrid dialects,mongolic languages of china
Advancing Sustainable Textiles in the Circular Economy through Innovative EPR Schemes (TRUSTEX)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Food, Bioeconomy Natural Resources, Agriculture and Environment
Theme: Bio-Environment
Destination: Other Bio-environnement destinations
Action type: Innovation actions
Countries involved: Luxembourg,Germany,Belgium,Türkiye,Ireland,Finland,Greece,Switzerland,Spain,France,Netherlands
Call: Circular solutions for textile value chains based on extended producer responsibility (HORIZON-CL6-2024-CIRCBIO-01, 2024)
Start date: 2025-01-01
End date: 2027-12-31
Duration (months): 36.0
Total project cost (EUR): 7614617.25
EU funding (EUR): 6999316.13
Participants: 21
Project status: SIGNED

Textile Extended Producer Responsibility (EPR) schemes represent a pivotal opportunity for the European Union to implement circular business models and solidify its position as a global sustainability leader within the textile industry. In the pursuit of this transformative goal, TRUSTex leverages invaluable insights from industrial partners and meticulous guidance from retail experts on the Stakeholder Board. The project's primary focus is to test strategies and formulate recommendations that analyze the feasibility of EPR schemes, pinpointing weaknesses, and proposing solutions. Based upon a robust governance foundation, TRUSTex adopts a holistic approach to cover the pivotal EPR activities; collection, sorting, reuse, and recycling. This multifaceted strategy is designed to identify barriers within these activities, showcasing innovations that directly improve the EU industry's capacity to manage textile waste. Additionally, TRUSTex includes upstream solutions that, while lying outside EPRs activities, have a direct influence on their feasibility. Consequently, the project integrates eco-design on new fibers and the incorporation of Design for Disassembly technologies. Additionally, TRUSTex will develop digital solutions aligned with forthcoming digital product passport and labeling regulations, to facilitate garment identification and traceability. Throughout the project, TRUSTex emphasizes continuous feedback and support from key stakeholders. This engagement ensures that recommendations not only maximize their impact but also align with the needs and secure compliance of industrial partners. In the final stages, a robust dissemination, communication, and exploitation strategy will reach society, consumers, and end-users increasing awareness about the benefits of adopting sustainable and circular business models. As a result, TRUSTex aspires to be a catalyst for positive societal impact, influencing health and livelihood practices across the continent.

Keywords: textile,extended producer responsibility,epr
Harnessing a novel CRISPR nuclease for programmable counterselection in human cells (CRISPR-SELECT)

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: Germany
Call: ERC PROOF OF CONCEPT GRANTS (ERC-2023-POC, 2023)
Start date: 2024-04-01
End date: 2025-09-30
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: SIGNED

CRISPR technologies have revolutionized genome editing in medicine, agriculture, biotechnology, and life-sciences research with their ability to generate virtually any DNA edit at any genomic site in any organism. However, editing frequencies can be restrictively low, requiring extensive screening to identify cells harboring desired edits. What remains elusive is a way to greatly boost the frequency of editing. While characterizing CRISPR-Cas systems, bacterial defense systems and the source of CRISPR technologies, we discovered a new mechanism that could kill unedited cells yet spare edited cells, regardless of the type of gene edit. This versatile and sequence-specific approach can be described as programmable counter-selection, as undesired cells are targeted for removal, and those cells can be targeted in a programmable way. If proven, this capability could radically boost the effective editing frequencies by removing unedited cells regardless of the underlying edit, providing much-needed relief to the burden of screening imposed on diverse genome-editing applications. Here, we propose to perform proof-of-concept experiments demonstrating this capability in human cells while exploring which editing applications would best benefit from this capability. The associated tasks build on my extensive work at the interface of CRISPR biology and technologies and leverages his numerous academic and industrial contacts. I ultimately aim to translate a novel biological insight from my group’s ERC Consolidator project into an innovative foundational technology, with a clear path toward its broad use in genome editing.
Distributed Inference for Energy-efficient Monitoring at the Network Edge (DIME)

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: Spain
Call: MSCA Postdoctoral Fellowships 2021 (HORIZON-MSCA-2021-PF-01, 2021)
Start date: 2022-06-01
End date: 2024-05-31
Duration (months): 24.0
Total project cost (EUR): 181152.96
EU funding (EUR): 181152.96
Participants: 1
Project status: CLOSED

Today, Internet of Things (IoT) sensors are being extensively used for monitoring processes/phenomena in smart cities. The data samples generated by these IoT sensors are wirelessly transmitted to servers at the network edge where compute-intensive Machine Learning (ML) models, specifically Deep Neural Networks (DNNs), are used for providing inference. However, a large percentage of data samples are redundant because they do not (significantly) improve inference. This leads to an excessive and unjustified carbon footprint of these systems as each redundant data sample will contribute to the Total System Energy (TSE) consumption. However, there is a lack of research on the design of these systems to reduce the TSE by considering the redundancy in the data. In DIME, we explore the TSE energy savings in a distributed inference setup by envisaging the deployment of the emerging small DNN models on the IoT sensors. My objective is to maximize TSE energy savings by answering two key questions: 1) when should an IoT sensor sample the process (to reduce redundant samples) and 2) where to do the inference on the sample, on the IoT sensor or at the edge server (to reduce TSE)? I will develop a general modelling framework and subsequently design and validate scheduling algorithms and sampling techniques that minimize the TSE by reducing the redundant data and maximize accuracy in ML-based monitoring systems. To achieve the objective, I will leverage my theoretical research experience on modelling and design and analysis of algorithms and the expertise of IMDEA Networks in applied machine learning and systems research. DIME directly contributes to reducing the carbon footprint of monitoring in smart cities, which is in line with the goal of Horizon Europe to achieve 100 climate-neutral smart cities by 2030.

Keywords: algorithms,distributed inference,energy optimization,edge intelligence,remote monitoring
Computational approaches to reveal the role of norepinephrine-astrocyte signaling in reinforcement learning (AstroError)

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,United States
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2025-05-01
End date: 2028-04-30
Duration (months): 36.0
Total project cost (EUR): 424992.0
EU funding (EUR): 424992.0
Participants: 2
Project status: SIGNED

Updating behavior based on environmental feedback is essential for organisms to learn and develop optimal behavioral strategies. Norepinephrine (NE) release from the locus coeruleus (LC) has been recently shown to critically mediate reinforcement learning (RL), such that a high level of NE released after a reward prediction error (RPE) improves subsequent behavior. However, the mechanism by which transient LC-NE neuron activity (lasting 50-300 ms) influences behavior several seconds later remains unclear. Building on experimental findings in the Outgoing Phase supervisor’s laboratory, we hypothesize that astrocytes play a previously unreported role in RL: they temporally amplify specific NE-RPE signals, facilitating the exploitation of neuronal sensory information when it is needed to optimize subsequent behavior. We will now probe the theoretical advantages of the novel NE-astrocyte RL mechanism by combining advanced computational analysis and modeling of rich neuronal and astrocytic cortical population recordings during RL. We will investigate computationally and theoretically the role of NE and astrocytes in modulating neuronal population dynamics supporting post-reinforcement behavioral gain. We will use data sets available in the Outgoing Phase supervisor’s laboratory to address three main scientific questions: 1) How do NE-RPE signals affect the future encoding of task-relevant information in neuronal populations? 2) How do in vivo neuro-astrocytic interactions support cross-population information processing critical for RL? 3) What is the role of NE-astrocytic signaling in RL - how is it biophysically supported, and implemented in novel RL algorithms and recurrent astrocyte-neuron networks (RANNs)? Altogether, I expect this proposal to be transformative in advancing our understanding of learning mechanisms in biological systems, and that they will potentially inform new algorithms for RL based not only on networks of neurons but also including astrocytes.

Keywords: astrocytes neural coding reinforcement learning prediction error information theory norepinephrine purinergic signaling
Neutrino Uncertainty Quantification using Neural Networks (NUQNET)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Fundamental Constituents of Matter
Panel description: Particle, nuclear, plasma, atomic, molecular, gas, and optical physics
Countries involved: Spain
Call: ERC STARTING GRANTS (ERC-2024-STG, 2024)
Start date: 2025-09-01
End date: 2030-08-31
Duration (months): 60.0
Total project cost (EUR): 1488481.0
EU funding (EUR): 1488481.0
Participants: 2
Project status: SIGNED

Neutrino oscillation experiments are entering a new era of precision, utilizing cutting-edge technologies and capabilities to offer unprecedented insights The ERC project NUQNET aims to enhance the prediction accuracy of neutrino-nucleus interactions by implementing advanced machine learning techniques. This endeavor involves incorporating predictive errors, a critical step necessary to meet the requirements and fully unlock the discovery potential of upcoming neutrino oscillation experiments. Presently, there is no theoretical method capable of consistently describing neutrino interactions across the wide energy spectrum investigated in neutrino experiments. Moreover, existing models in the market rely either on some approximations or on semi-phenomenological approaches, making it challenging to rigorously assess the theoretical uncertainty. This uncertainty must be meticulously propagated throughout the analysis to extract precise oscillation parameters. The PI plans to go beyond the limitations of traditional many-body techniques by using ANN architectures to represent the nuclear wave functions and obtain the spectral function of several nuclei relevant for oscillation experiments (see next section for details). A novel methodology to incorporate quantum-mechanical effects into the description of final state interactions, a domain typically modeled by semi-classical intra-nuclear cascades in neutrino event generators, will be introduced. To achieve this goal, the PI will pioneer the development of a new real-time Variational Monte Carlo algorithm, building upon the formalism employed to obtain the ANN nuclear-wave functions. New tools will be developed to describe the high-energy region relevant for DUNE in which the degrees of freedom switch from nucleons and pions to partons. Finally, the PI will coordinate a pioneering effort to estimate the uncertainty of the theoretical predictions needed for oscillation analysis and to be able to discover new physics.

Keywords: neutrino nucleus cross sections electroweak interactions uncertainty quantification in neutrino oscillation experiments nuclear structure and dynamics artificial neural network quantum states real time dynamics
General Relativistic numerical models of accretion disks and magnetic reconnection with the PLUTO code (GR-PLUTO)

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: Italy
Call: MSCA Postdoctoral Fellowships 2021 (HORIZON-MSCA-2021-PF-01, 2021)
Start date: 2022-11-01
End date: 2024-10-31
Duration (months): 24.0
Total project cost (EUR): 188590.08
EU funding (EUR): 188590.08
Participants: 1
Project status: CLOSED

Magnetic fields shape the dynamics of relativistic plasmas that orbit around astrophysical black holes (BHs), as observed in current general relativistic magnetohydrodynamic (GRMHD) models. A primary example is magnetic reconnection, i.e. the rearrangement of the structure of magnetic field lines, which converts magnetic energy into kinetic and thermal energy and accelerates particles to relativistic velocities, thus producing the high-energy non-thermal radiation observed from accreting compact objects. Despite the importance of small-scale instabilities in determining the properties of magnetic dissipation and particle acceleration, global simulations of relativistic accretion disks usually consider either infinitely conducting fluid or one with a constant finite magnetic dissipation, which doesn't capture the collisionless nature of astrophysical plasmas. The goal of the GR-PLUTO project is to perform the first GRMHD numerical study on reconnection in relativistic disks using an effective non-constant resistivity, which will go beyond standard fluid models by introducing new non-collisional effects in global GRMHD simulations. This approach will test our current knowledge of relativistic magnetic reconnection and provide more consistent estimates for the rate at which particles can be accelerated, along with the general structure of magnetized accretion flows around BHs where this process occurs. As the simulations required to quantify the improvements w.r.t. state-of-the-art relativistic MHD reconnection and GRMHD accretion flow models have a high computational cost, the action will include the development of a numerical tool for the modeling of GRMHD flows based on the public PLUTO code. In collaboration with the hosting group at UniTo, the action will produce a new code that will combine the benefits of highly accurate numerical schemes with energy-efficient HPC methods, which will continue to be freely accessible by the public.

Keywords: magnetohydrodynamics magnetic reconnection plasma physics accretion disks general relativity black holes numerical methods for astrophysics gpus high performance computing
Security enhancement through heterogeneous data fusion and improved AI/ML-powered Copernicus maritime and border surveillance services (AI4COPSEC)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Digital, Industry and Space
Theme: Space
Destination: Open Strategic Autonomy in Developing, Deploying and Using Global Space-Based Infrastructures, Services, Applications and Data
Action type: Research and Innovation actions
Countries involved: Belgium,Greece,France,Spain,Italy,Portugal,Norway
Call: Copernicus for Security (HORIZON-CL4-2024-SPACE-01, 2024)
Start date: 2024-12-01
End date: 2027-11-30
Duration (months): 36.0
Total project cost (EUR): 5086599.18
EU funding (EUR): 4044012.95
Participants: 11
Project status: SIGNED

The Copernicus Security Service (CSS) is instrumental to support the European Union’s security, safety, law enforcement, and international commitments with the European Commission advocating for its advancement. The AI4COPSEC project aims to demonstrate the potential of advanced ML and GeoAI models to provide relevant intelligence for enhancing existing Copernicus products and services such as oil spill response, illegal fishing alert and create new services intended to support Search-and–Rescue operations and irregular migration detection. The project will enhance CSS services through the usage of self-supervised deep learning models, geomatics and social media data (OSINT) extracted from heterogenous multi-sources data. AI4COPSEC is expected to have a transformative effect on the operational capabilities of EU security,surveillance and safety by leveraging advances to enhance the timeliness, accuracy, and relevance of information derived from EO and non-EO data sources, including the innovative use of thermal imagery for ship detection, the combination of satellite automatic identification system (AIS) data with structured data extracted from social media sources and the usage of in-situ environmental measurement derived from AIS data and IoT devices. This integration will lead to more effective and efficient security operations, environmental monitoring, and disaster response. AI4COPSEC is also set to detect anomalies and threat in the maritime traffic and advance ship detection capabilities, utilising high-resolution optical images and advanced segmentation algorithms to enhance the detection of small vessels independently of the type of material (metallic structures, wood, rubber...), a critical factor for a comprehensive maritime surveillance in line with the CSS and the EU's maritime security strategy (EUMSS).

Keywords: ml,geoai,ais,eo and non eo data,osint data,ocean conditions,sar,oil spill response,irregular migration,illegal fishing,ai,data fusion,non space data,maritime surveillance
High-speed timE Resolved fluorescence iMaging with no pilE-up diStortion (HERMES)

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: Italy
Call: ERC STARTING GRANTS (ERC-2023-STG, 2023)
Start date: 2024-01-01
End date: 2028-12-31
Duration (months): 60.0
Total project cost (EUR): 1500000.0
EU funding (EUR): 1500000.0
Participants: 1
Project status: SIGNED

The overarching goal of HÈRMES is to establish a new methodology for time-resolved imaging by means of Time-Correlated Single Photon Counting(TCSPC). Since its debut in the literature, the potential of TCSPC as non-invasive, ultra-sensitive and extremely precise imaging tool was manifest. Numerous applications have benefited from it so far, but a key limitation still prevents its wide use in many other crucial applications: the speed of a TCSPC acquisition chain must be kept low(few Mcount/s in the best case) to avoid distortion due to pile-up of events. Up to now researchers in this field have tried to work around this limit mainly by posing multiple channels in parallel, but still facing severe limitations mainly due to efficiency, fill factor, precision, linearity and readout complexity. I propose an innovative methodology for removing all constraints on TCSPC, promising a change in the paradigm of how TCSPC systems are conceived and how time-resolved measurements are carried out. To achieve such an ambitious goal, a radical change is necessary. I expect to develop a comprehensive mathematical model showing that pile-up distortion can be avoided with any combination of single photon detector and laser excitation power if additional picosecond-precision information on the status of system in each time bin is acquired at run-time. I will develop ultrafast electronics for Single Photon Avalanche Diodes to move from theory to the real experimental world. Moreover, I will empower the new constraint-less TCSPC by developing an innovative computational imaging framework, opening the way to the real-time acquisition of 4D images. Next-generation TCSPC systems based on the HÈRMES methodology will allow the exploitation of this powerful tool in crucial applications such as, for example, intraoperative and neuron imaging, where an ultrafast but still linear acquisition is necessary to enable complex operations like image-assisted brain surgery and spike analysis of neurons.

Keywords: single photon,time correlated single photon counting,tcspc,time resolved imaging,computational imaging