Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
A 3D-AI solution for Scalable and Accurate Inspection of Manufactured Parts at all Stages of Automotive Manufacturing (3D-AI)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Other destinations
Action type: EIC actions
Countries involved: Israel
Call: EIC Accelerator 2024 - Short application (HORIZON-EIC-2024-ACCELERATOR-01, 2024)
Duration (months): 24.0
Total project cost (EUR): 2500000.0
EU funding (EUR): 2500000.0
Participants: 1
Project status: SIGNED

The automotive industry faces significant challenges in improving quality, reducing costs, and shortening production setup times, all of which require effective inspection processes. Current dimensional inspection methods, such as manual inspections using measurement machines(CMM), are slow and error-prone, while automated systems are costly, complex, and take up valuable floor space, limiting their usgae to end-of-line checks that may catch defects too late or not catch them at all (due to limited visibility). These inefficiencies result in $30 billion in annual losses from scrap, delays, reworks, and recalls. Existing solutions often fail to detect defects early in production, further increasing costs and inefficiencies. To address these challenges, we developed "3D-AI," a pioneering software technology that transforms affordable, standard cameras into highly accurate inspection instruments. By utilizing sophisticated computer vision, AI algorithms, GPU processing, and digital twin technology, 3D-AI enables continuous, real-time inspections with sub-millimetre precision throughout the entire manufacturing process. This capability facilitates the early detection of defects, thereby reducing the necessity for expensive reworks and recalls and significantly enhancing quality control in automotive production. Our 3D-AI stands out from existing solutions by eliminating the requirement for individual “AI training” for each part, ensuring straightforward deployment and maintenance. It achieves remarkable accuracy, measuring up to 1 mm RMS over a 5-meter range with a confidence level exceeding 99.7% (“Six Sigma”) which is not achievable by standard AI technologies, while lowering costs and reducing system complexity enabling deployment at scale. In the planned EIC project, SkillReal will enhance technology accuracy, and validate robot calibration with Siemens software at OEM facilities, followed by a European market launch supported by Siemens and local integrators.

Keywords: deep learning,computer vision,augmented reality,industry 5 0,digital twin
Transformative Digital Air Vehicle with IoT Sensors for Safer Urban Skies (AVATAR)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Mobility
Destination: Clean and competitive solutions for all transport modes
Action type: Research and Innovation actions
Countries involved: Czechia,Denmark,Luxembourg,United Kingdom
Call: Digital aviation technologies for new aviation business models, services, emerging global threats and industrial competitiveness (HORIZON-CL5-2022-D5-01, 2022)
Start date: 2023-02-01
End date: 2026-01-31
Duration (months): 36.0
Total project cost (EUR): 2890339.25
EU funding (EUR): 2890339.25
Participants: 7
Project status: SIGNED

AVATAR is dedicated to developing a digital twin platform to enable continuous monitoring of the vehicle during its service life for the purpose predictive maintenance. AVATAR’s digital twin model acts in parallel to the real structure and provides, where necessary, an early warning of operational behaviour that deviates from expected (simulated). Additionally, by recording the actual load spectrum that each air vehicle experiences during every flight, it will optimize the individual service life, which contributes towards a sustainable air mobility. AVATAR’s will contribute to an increase safety of urban skies and has the potential to optimise the design and performance of air vehicles by providing real load data for each vehicle. The IoT sensing skin proposed by AVATAR is a key enabler for real-time critical operational information acquisition and connectivity between the physical aircraft and the digital counterpart through wireless communication. AVATAR DT platform will provide a seamless integration of IoT, big data and machine learning to collect, compute and predict. AVATAR will conform to the modern approach towards developing a digital twin based on different types of data and advanced analytical methods which eliminates the need for the real air vehicle and the virtual models to be directly connected and builds this connection through ML and AI. AVATAR contributes to the acceleration of digital transformation of air mobility, particularly in urban setting.

Keywords: digital twin,iot,sensing skin,structural health monitoring,predictive maintenance,advance analytics,cloud base solutions,big data,diagnosis and prognosis
Add in choice: optimizing students' dynamic and interdependent decisions (EDDYNCHOICE)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Individuals, Markets and Organisations
Panel description: Economics, finance , management
Countries involved: France
Call: ERC STARTING GRANTS (ERC-2024-STG, 2024)
Start date: 2025-03-01
End date: 2030-02-28
Duration (months): 60.0
Total project cost (EUR): 1447000.0
EU funding (EUR): 1447000.0
Participants: 2
Project status: SIGNED

The diversity of educational opportunities has the potential to improve the situation of everyone by providing an environment, adapted to the needs and goals of all. Letting parents or students choose their trajectory can improve welfare as it allows them to sort into the right program, using their private information on comparative advantages and preferences. However, personal choices are not necessarily in line with society’s objectives because of externalities. Apart from financial externalities that are inevitable in subsidized educational systems, peer effects are crucial in this context as the student composition of an educational program affects its quality. Furthermore, from an individual perspective, the right choice is difficult to make, and behavioral biases (or “internalities”) are common. As a result, such choices are often restricted. Targeted vouchers open up spots for some groups of children at the cost of others, grading standards prevent students from attending certain tracks or courses in high school, and college admissions prevent completely free access. However, there is little guidance for policymakers in knowing and realizing the optimal size and composition of different options. What is the right balance between choice, ability and diversity requirements in school choice and tracking? Are seats in college programs optimally provided according to society’s needs? How to incentivize students to align their choices with a societal optimum? This project aims to develop a methodology to empirically analyze the optimal provision of choice using novel dynamic discrete choice models that take into account externalities and internalities. The objective is to apply this methodology to rich micro-data from the three main levels of education: school competition in primary education, tracking in secondary education, and admission and graduation in college.

Keywords: education economics,labor economics,structural modeling,dynamic discrete choice,peer effects,school matching,early tracking,production function
European commoN EneRgy dataSpace framework enabling data sHaring-driven Across- and beyond- eneRgy sErvices (ENERSHARE)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Energy
Destination: Sustainable, secure and competitive energy supply
Action type: Innovation actions
Countries involved: Italy,Germany,Luxembourg,Spain,Portugal,Slovenia,Norway,France,Greece,Finland,Latvia,Netherlands
Call: Establish the grounds for a common European energy data space (HORIZON-CL5-2021-D3-01, 2021)
Start date: 2022-07-01
End date: 2025-06-30
Duration (months): 36.0
Total project cost (EUR): 9087822.5
EU funding (EUR): 7645511.75
Participants: 31
Project status: SIGNED

The ongoing energy system digitization is making available an enormous amount of data, paving the way for data sharing-enabled cross-value chain services, which may contribute to system-level increased efficiency and hence facilitate the energy transition. However data sharing in the energy sector is lagging behind, mainly due to lack of trust, privacy breaches risk and business models immaturity. In that respect ENERSHARE will a) deliver a Reference Architecture for a European Energy Data Space, which hybridizes SGAM with IDSA and GAIA-X architectures, by bringing data value chain perspective into the energy one b) evolve interoperability, trust, data value and governance building blocks to TRL 6-7 IDSA-compliant ones, adapt them to energy sector, and deploy: 1) across-energy and cross-sector data enhancement technology enablers and standardizable interfaces and open APIs by leveraging on open Standards (e.g. ETSI Context Broker) and ontologies (e.g. SAREF 2) trust-related connectors, to ensure privacy, confidentiality, cybersecurity-preserving trust, sovereignty and full control of data 3) Blockchain/Smart contract-enriched marketplace for data versus energy assets/services coordination, sharing, exchange, and beyond financial compensation 4) cross-value chain value-added services and Digital Twins, by leveraging on privacy-preserving federated learning c) integrate and deploy them within a Reference Implementation of a European Energy Data Space, which will be demonstrated along 7 pilots and 11 intra-electricity, intra-energy and beyond energy use cases d) co-design SSH-based consumer-centric business models for energy data sharing enabling data beyond-financial value creation and spreading along value chain d) prepare the ground for the European Energy Data Space setup, through alignment with EU-level relevant initiatives (GAIA-X, IDSA, BDVA, ETIP SNET, BRIDGE), contributing to Data Space standardization and boosting a level playing field for data sharing.

Keywords: cross sector interoperability open apis trusted sovereign energy data sharing consumer centric data value business models blockchain based marketplace digital twins
Integrating Adaptive Learning in Maritime Simulator-Based Education and Training with Intelligent Learning System (i-MASTER)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Culture, creativity and inclusive society
Theme: Culture, creativity and inclusive society
Destination: Innovative Research on Social and Economic Transformations
Action type: Research and Innovation actions
Countries involved: Norway,Sweden,Germany,Finland,Austria
Call: Integration of emerging new technologies into education and training (HORIZON-CL2-2021-TRANSFORMATIONS-01, 2021)
Start date: 2022-09-01
End date: 2026-08-31
Duration (months): 48.0
Total project cost (EUR): 3286600.0
EU funding (EUR): 3286600.0
Participants: 8
Project status: SIGNED

While all educational sectors have been affected by the COVID-19 pandemic, the vocational-oriented academic training components in higher education sectors have been more severely affected than others. For undergraduate and graduate students studying for a career at sea, the pandemic has led to suspension to their simulator training sessions, vocational learning opportunities and future careers. The simulator-based education and training domain, which forms the platform of skilled manpower supply for the maritime industry, faced unprecedented challenge to ensure the continuity of the educational activities and to cope with the constraints imposed by the pandemic. High-quality vocational education is the cornerstone of effective youth transitions into the labour market for the European society. The disruption of simulator-based training and tutoring sessions has led to concerns regarding whether the higher and vocational educational institutes can produce a sufficient and competent workforce who could adequately carry out on-board navigation functions safely and efficiently in the near future. By accounting for the needs, knowledge gaps and challenges faced by today’s maritime education and training sector, the i-MASTER project aims to integrate emerging technologies in vocational education and training to develop an innovative Intelligent Learning System (ILS) with maritime learning analytics and adaptive learning function to facilitate both remote and on-site maritime simulator-based education and training. The i-MASTER solution will significantly enhance the effectiveness and accessibility of simulator-based education in the European society and further improve safety, security and performance of maritime operations of the future.

Keywords: vocational education and training intelligent learning system adaptive learning
Optimization of Free Space Optical Communication Link based Drones for Search and Rescue Operations in the Arctic Region (FSOCFORSAR)

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: Norway
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): 226751.04
EU funding (EUR): 226751.04
Participants: 1
Project status: CLOSED

The Arctic region is witnessing more frequent shipping accidents than ever which require accurate search and rescue operations. The existing communication systems use drones with radio frequency (RF) technology, which has limited bandwidth and cannot offer 4G/5G connectivity due to harsh weather constraints. It further limits the capacity of drones and affects the resolution of images and videos. The current research proposes to design free space optical communication (FSOC) link-based drones that can enhance connectivity between multiple drones and rescue ships as well as the capacity of drones to produce high resolution images and videos. The proposed FSOC-based drones can provide highspeed connectivity than the existing RF technology. The research will collect atmospheric turbulences data from the Arctic region and use it for designing the FSOC link. Furthermore, to enhance the capacity of FSOC-based drones, the research will design advanced multiplexing schemes (Mode Division Multiplexing and Orthogonal Code Division Multiple Access) and to ensure link stability, omnidirectional receiver will be designed. The research will further evaluate the performance of proposed FSOC link under the impact of various atmospheric turbulences. The expected outcomes of this research will lead to the optimisation of FSOC-based drones in expanding search areas and improving communication systems for SAR operations in the Arctic region.

Keywords: free space optics,drones,sar,mode division multiplexing,optical code division multiple access
Sorting and export of mucins. (MuRe)

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: Spain
Call: MSCA Postdoctoral Fellowships 2021 (HORIZON-MSCA-2021-PF-01, 2021)
Start date: 2023-02-01
End date: 2025-01-31
Duration (months): 24.0
Total project cost (EUR): 175674.96
EU funding (EUR): 175674.96
Participants: 1
Project status: CLOSED

Inflammatory Bowel Diseases (IBD) affected more than 6.8 million people worldwide in 2017, and Chronic Obstructive Pulmonary Disease(COPD) caused 3.23 million deaths in 2019. Both illnesses present a disturbance in the quantity and quality of mucus due to an abnormal expression and secretion of mucins. The mucins are the main component of mucus and are specifically expressed in cells and tissues. Mucin secretion is crucial for the normal physiology of wet mucosal epithelium. Growing data shows that alterations in mucins expression and their localization are related to pathological processes, such as inflammation, infection, autoimmunity, and cancer. All mucins are synthesized in the endoplasmic reticulum and exported to the Golgi complex, and after extensive glycosylation, are transported to the plasma membrane. However, how these mucins are sorted and packed into granules that can reach micrometers in size remains largely unknown. I hypothesize that transmembrane and secreted mucins are sorted and packed in separate containers by regulatory partners, different from those involved in other bulky-cargo pathways – as collagen –. The Malhotra lab is a world-leader in the study of protein secretion. Recently, I have tagged mucins with fluorescent proteins by CRISPR/Cas9 gene-editing technology, which will allow me to understand these glycoproteins from synthesis to functional target. Unveiling this fundamental knowledge could stimulate the development of therapies aimed to modulate the expression and precise release of mucins, both in pathologies due to mucin hyposecretion – such as IBD – and hypersecretion – observed in COPD –.

Keywords: mucins protein sorting protein trafficking bulky cargoes
Physiological and evolutionary responses of cyanobacteria in a sustainable Mars exploration program (MarCyano)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Universe Sciences
Panel description: Astro -physics/ -chemistry/ -biology; solar system; planetary systems; stellar, galactic and extragalactic astronomy; cosmology; space sciences; astronomical instrumentation and data
Countries involved: Germany
Call: ERC STARTING GRANTS (ERC-2024-STG, 2024)
Start date: 2025-02-01
End date: 2030-01-31
Duration (months): 60.0
Total project cost (EUR): 1563750.0
EU funding (EUR): 1563750.0
Participants: 1
Project status: SIGNED

The world’s major space agencies share a goal of sending humans to Mars within the next few decades. A sustainable human presence there, akin to today’s presence in Antarctica, could generate paradigm-shifting knowledge at an unprecedented pace. One major challenge lies in providing the food, oxygen and other necessary consumables: these cannot be shipped from the Earth over the long term. They should instead be produced on site and for this, Nostocaceae cyanobacteria can be instrumental: fed with materials available in the Martian ground and atmosphere, they could provide feedstock for a range of bioprocesses which, in turn, could produce a wide range of consumables. However, fundamental knowledge is lacking to turn this concept into practical solutions: the physiology of cyanobacteria in the foreseen cultivation conditions is poorly understood. These conditions combine low total pressures, low partial pressures of dinitrogen, interactions with a basaltic substrate from which nutrients are leached, and high concentrations of chaotropic salts. In the proposed project, I will enable a deep understanding of cyanobacterial physiology in these conditions. For this I will employ a unique approach combining cultivation assays in unique hardware previously developed by my team, adaptive laboratory evolution, cutting-edge omics technologies, the development of a mathematical model which can predict the productivity and efficiency of cyanobacterium cultivation from Martian resources, and the construction of a testbed for the experimental validation of this model. I will use the generated knowledge and tools to design, characterize and demonstrate the viability of bioprocesses which dramatically improve the odds that Mars exploration is done in a sustainable way. Finally, I will adapt the concepts developed for bioproduction on Mars to open new avenues for sustainability on Earth, which I plan to follow up by applying for an ERC Proof of Concept Grant.

Keywords: microbiology human space exploration
Development and characterization of a novel CAR T-cell therapy for NHL (CARLY)

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: Spain
Call: MSCA Postdoctoral Fellowships 2023 (HORIZON-MSCA-2023-PF-01, 2023)
Start date: 2025-01-01
End date: 2027-06-30
Duration (months): 30.0
Total project cost (EUR): 226441.2
EU funding (EUR): 226441.2
Participants: 2
Project status: SIGNED

Non-Hodgkin lymphomas (NHL) are a group of blood cancers characterized by the expansion of malignant B lymphocytes creating tumors throughout the body. The standard of care for patients with aggressive NHL is a combination of chemotherapy and immunotherapy. Even though it is well tolerated, ~40% of the patients respond poorly and eventually relapse. Novel personalized treatment options are now available for such individuals and engage patients’ own T cells genetically manipulated to express a chimeric antigen receptor (CAR) capable to recognize and attack malignant cells. Thus far, CAR-T products recognizing and attacking cells expressing the protein CD19 have been approved by regulatory agencies across the planet. However, long-term follow-up studies show that more than half of these patients eventually succumb to relapse after CAR-T treatment. One of the causes for this is the loss of the CD19 protein on NHL cells. Therefore, finding other proteins beyond CD19 that could be targeted or co-targeted with CAR-T-cells is desired. Indeed, in the proposed project, I intend to develop and characterize a CAR-T therapy directed against a cell surface protein highly expressed in NHL (preliminary data). Herein, I plan to evaluate the levels of the new target protein in a large cohort of NHL patients using immunohistochemistry and to perform a thorough preclinical assessment of this novel CAR-T-cell approach using cutting-edge preclinical NHL models. To achieve these goals, I will exploit the experience of the hosting institution in generating NHL patient-derived xenografts and in the bench-to-bedside CAR-T development process, as well as the external collaborators who are experts in blood cancer treatment. This fellowship will significantly expose me to world-class theoretical and hands-on preclinical research in hematooncology and adoptive cell therapy, thus boosting my career as an independent researcher.

Keywords: immunotherapy,car t cells,adoptive cell therapy,non hodgkin lymphoma,