Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Robust and data-Efficient Learning for Industrial Control (RELIC)

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,Poland
Call: MSCA Postdoctoral Fellowships 2021 (HORIZON-MSCA-2021-PF-01, 2021)
Start date: 2023-06-05
End date: 2025-06-04
Duration (months): 24.0
Total project cost (EUR): 210911.04
EU funding (EUR): 210911.04
Participants: 2
Project status: CLOSED

Increasing energy and resource efficiency in industrial systems is key to decrease harmful emissions by 90% by 2050. Reaching the environmental targets requires a holistic approach to how resources and energy are delivered to the industry by means of distribution networks, such as heat networks, electricity networks, or gas transport networks. I will devise new control strategies that ensure robust operation of distribution networks while ensuring safety and satisfaction of environmental objectives. The environmental performance of the whole system hinges on the performance of distribution networks. Optimal control of such networks is complex due to timescales, from milliseconds to ensure safe operation of pumps or generators, to days or months to include environmental goals, spatial complexity, uncertainty related to varying operating conditions, incomplete information available, and limited computational power. Existing control frameworks are usually application specific and have limited use in large-scale systems. In the project, I will advance theory in data analytics and optimisation, and build on my industrial experience to develop operating strategies for distribution networks that will enable safe implementation and reaching the environmental targets. There is a potential in integrating machine learning in control design to overcome the complexity while satisfying safety constraints, as shown in robotics and automotive industry. However, IPCC indicated that "The key challenge for making an assessment of the industry sector is the diversity in practices, which results in uncertainty, lack of comparability, incompleteness, and quality of data available in the public domain on process and technology specific energy use and costs". The research question I will address in this project is if and how incorporating data-driven learning in design of control algorithms leads to improved environmental performance and safe operation of large-scale industrial networks.

Keywords: model predictive control,robust control,optimisation under uncertainty,gas transport
Oligomeric Plasticity Gating Mechanisms in Ion Channels (OLIGOMERS)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Molecules of Life: B iological  Mechanisms , Structures  and Functions
Panel description: For all organisms : Molecular biology , biochemistry, structural biology, molecular biophysics , synthetic and chemical biology, drug design, innovative methods and modelling
Countries involved: Israel
Call: ERC STARTING GRANTS (ERC-2025-STG, 2025)
Start date: 2025-09-01
End date: 2030-08-31
Duration (months): 60.0
Total project cost (EUR): 1500000.0
EU funding (EUR): 1500000.0
Participants: 1
Project status: SIGNED

Ion channels are transmembrane proteins regulating ion flux across cell membranes and are crucial for numerous physiological processes. Hence, ion channels are implicated in many diseases. To further drug development targeting ion channels, a comprehensive understanding is needed not only of their low-energy abundant states, but also of their rare and transient high-energy states, which play critical roles in channel function, regulation, and folding. The cutting-edge high-speed atomic force microscopy (HS-AFM) technique allows now to study such states, by providing simultaneously, on a single-molecule level, both dynamic and structural information of freely diffusing ion channels in the membrane, a feat that is currently impossible with other methods. Recently, I discovered using HS-AFM that the TRPV3 channel possesses a rare and transient pentameric state that reversibly interconverts with its canonical tetrameric sate, potentially conferring it new function. Here, I aim to investigate further the functional role and biological relevance of this finding, while concomitantly investigating if similar oligomeric plasticity occurs in other ion channels of diverse functions. Most particularly, I aim to investigate whether reversible oligomeric changes could be an additional mechanism for ion channels to modulate function. To do so, I will: 1) Discover reversible oligomeric transitions in other ion channels. 2) Reveal how reversible oligomeric transitions could modulate function. 3) Assess the physiological relevance and feasibility of such transitions. Positive results will lead to a paradigm shift in the way we think about the assembly, dynamics, and function of ion channels and introduce a novel new concept of how ion channels undergo conformational changes. Additionally, results will be of high impact to translational applications such as drug development and drug delivery and establish HS-AFM as a powerful complementary method in the field of structural biology.

Keywords: ion channels,oligomeric state,cryo em,hs afm,rare and transient states
North-south EXchange for Underground Science (NEXUS)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Staff Exchanges
Action type: Marie Skłodowska-Curie actions
Panel: Physics
Countries involved: France,Italy,South Africa,Canada,United Kingdom,Spain,United States,Australia
Call: MSCA Staff Exchanges 2024 (HORIZON-MSCA-2024-SE-01, 2024)
Start date: 2026-01-01
End date: 2029-12-31
Duration (months): 48.0
Total project cost (EUR): 771540.0
EU funding (EUR): 771540.0
Participants: 17
Project status: SIGNED

The nature of invisible dark matter (DM), which constitutes ~26% of the mass-energy balance of the Universe, remains a major puzzle in physics. Despite extensive searches, DM constituents remain undetected, though experiments have significantly constrained the allowed parameter space. The NEXUS project aims to advance this research by enhancing detector performance, refining instrumentation, and expanding theoretical insights through a sustainable expert network. Partnering with world-class laboratories in Europe, the USA, Canada, Australia, and South Africa, NEXUS will tackle extraordinary experimental and theoretical challenges. This research relies on Underground Laboratories (ULs), where cosmic rays are suppressed by ~1000 meters of rock, minimizing the cosmogenic backgrounds. The proposed NEXUS initiative aims to establish a global network of ULs dedicated to ground-breaking advancements in the field. The collaborative approach undertaken in NEXUS is designed to provide a dynamic environment for advances in ultra-sensitive detectors and ultra-low radiation techniques, ready to lead innovation in both the global search for rare events and cutting-edge technological development ultimately benefiting society and industry. ULs’ support for research will provide: (i) effective radiation shielding; (ii) above-ground and underground support facilities, such as clean (radon-free) rooms, radio-purity assay equipment, and cryogenic equipment; (iii) material production and purification facilities; (iv) tools and methods to characterize underground facilities and related instrumentation; and (v) a unique environment for multidisciplinary research. In addition, efforts are being made to further increase instrument sensitivity to meet new challenges in rare-event research. NEXUS will pave the way for ground-breaking discoveries, fostering a deeper understanding of the universe while driving technological advancements with broad societal and industrial benefits.

Keywords: astroparticles,cosmology,underground laboratories,radioactivity,cosmic background,low background,quantum computing,modelling
The impact of disturbances on the productivity and biomass of European forests (PARDI)

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: Environment and Geosciences
Countries involved: Denmark,France
Call: MSCA Postdoctoral Fellowships 2022 (HORIZON-MSCA-2022-PF-01, 2022)
Start date: 2024-06-01
End date: 2026-05-31
Duration (months): 24.0
Total project cost (EUR): 214934.4
EU funding (EUR): 214934.4
Participants: 2
Project status: SIGNED

Forests account for most of the global terrestrial carbon sink. Yet, they are exposed to an increasing stress from climate-induced disturbances (wild fires, droughts, storms, insect outbreaks) and land-use pressure (deforestation, intensive forestry, expansion of plantations ). Due to the coarse resolution of the the past generation of satellites (MODIS, Landsat), the severity, extent and the timing of these disturbances are not accurately assessed across the European continent, which limits our understanding of forest resistance and recovery in the face of climate extremes, compound events and human-induced disturbances. A new generation of satellite constellation (PlanetScope) providing very high resolution imagery at 3 meters resolution daily has been launched in 2016, and reached full capacity to map small scale disturbances down to tree level. The PARDI (PARtial DIsturbances) project will take advantage of this unique dataset and make a giant leap to quantify and understand the impact of disturbances on the mortality, biomass, carbon loss and recovery of European forests. PARDI will go beyond the state of the art by providing an unprecedented spatial resolution (from 30 m in the most recent literature to 3 m in this project), thus reducing the uncertainty on productivity and biomass changes. The outcomes of PARDI will help to assess the sensitivity of European forests to climate change, and the vulnerability of key ecosystem services such as carbon sinks and wood production.
SCALING UP PHARMACEUTICAL 3D PRINTING FOR PERSONALISED MEDICINE (Pharma3DP)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Other destinations
Action type: EIC actions
Countries involved: Spain
Call: EIC Accelerator 2024 - Short application (HORIZON-EIC-2024-ACCELERATOR-01, 2024)
Duration (months): 24.0
Total project cost (EUR): 1338286.0
EU funding (EUR): 1338286.0
Participants: 1
Project status: SIGNED

Compounding is a common method to reach non-commercial dose targets for treatments that require personalisation (low therapeutic index drugs and for paediatrics). This involves weighing powders, splitting tablets, taking multiple tablets and filling capsules by a specialist pharmacist. Despite being an €9.5B market worldwide with CAGR of 8.6% (2023-2033, persistencemarketresearch.com), compounding is still a time and resource intensive process and is prone to human error such as contamination and inaccuracy. Pharmaceutical 3D printing for personalised medicine solves these issues by automating the process. It is an exponentially growing field with more and more hospitals, pharmacies, universities and pharmaceutical companies getting involved every year. However, there is a lack of preparation for new legislation and field growth. FABRX-AI proposes novel software solutions and pre-filled pharma-ink (pre-printed formulation) cartridges for an innovative hub-and-spoke, all-in-one solution that will enable distributed pharmacies across all geographical areas (spokes) to improve their printing and compounding capabilities. Access to regulatorily validated printing protocol libraries will be enabled. Individuals involved in protocol development will be financially reimbursed to encourage continued development for more treatments. FABRX-AI's software will save all print sessions to hub servers to facilitate full, automated auditability and regulatory compliance. FABRX-AI will also scale up the manufacturing of its pre-filled pharma-ink cartridges to enable simplified pharmaceutical compounding workflows for easy use and implementation. This project will give more patients access to the best treatment personalised for their exact needs.

Keywords: pharmaceutical 3d printing,pharmaceutical compounding,automated compounding,personalised medicine,precision medicine,decentralised manufacture
Development of electronic functions for plastic, fabric, and composite materials, using the additive EOPROM® paste. (EOPROMFLEX)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Other destinations
Action type: EIC actions
Countries involved: France
Call: EIC Accelerator 2024 - Short application (HORIZON-EIC-2024-ACCELERATOR-01, 2024)
Duration (months): 24.0
Total project cost (EUR): 2450000.0
EU funding (EUR): 2450000.0
Participants: 1
Project status: SIGNED

With the composite structures well established, the industry is now looking for solutions to monitor the structures, preventing damage and requirement of regular maintenance. The solution for ensuring their safety and reliability is to develop smart composites consisting of structural health management (SHM) and Prognostic Health Management (PHM) systems including integrated electronic components that can monitor and diagnose/detect faults in the structures, and provide real-time monitoring, respectively. However, the current use of sensors on the surface and within the composite structures is not widely applied in the industry because designers still consider the sensor as an implanted defect that could be the site of initiation of delamination, and more likely leading to deterioration of the composite materials. MCVE Technologie, with its EOPROM formulation and EOPROMFLEX process consisting of the continuous deposition of a copper-based paste on various substrates (composites, plastics, and fabrics), in a high-speed Roll-to-Roll manufacturing process for the industrial scale manufacturing of Flexible Hybrid Electronics (FHE) provides a safe, reliable, efficient and affordable SHM solution

Keywords: printed technology,smart fabrics,structural health management,prognostic health management
Age-dependent nutrient priming of the pre-metastatic niche (PrimeAge)

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: Belgium
Call: MSCA Postdoctoral Fellowships 2023 (HORIZON-MSCA-2023-PF-01, 2023)
Start date: 2024-10-01
End date: 2026-09-30
Duration (months): 24.0
Total project cost (EUR): 191760.0
EU funding (EUR): 191760.0
Participants: 1
Project status: SIGNED

Metastasis is the leading cause of death in cancer patients. Aging is an important risk factor for metastasis formation, yet the underlying processes remain poorly understood. Given the increase in aging populations worldwide, elucidating mechanisms of age-promoted metastasis formation is an important and timely topic addressed in this proposal. Recently, the Fendt group has pioneered the concept that tumor-secreted factors (TSF) induce nutrient priming of distant organs for metastasis formation. This concept is exemplified by adaptations in lipid metabolism of target organs, triggering a pro-metastatic signaling pathway in invading tumor cells. I propose that nutrient priming is age-dependent, as the metabolism changes with age, contributing to the enhanced risk of metastasis formation in the elderly. I will investigate this concept using melanoma as a prototypic cancer of age-dependent, aggressive metastatic behavior. I will address 3 specific objectives: (1) Identify age-dependent metabolic response of distant organs to TSF using mass spectrometry imaging-based spatial metabolomics in young and aging mouse models, and corroborate my findings in melanoma patients. (2) Elucidate molecular mechanisms whereby identified metabolites promote the pre-metastatic potential of the distant niche by applying genetic screens as well as multi-omics and functional assays to spheroid models of metastasis. (3) Validate the therapeutic potential of intercepting age-specific metabolic priming using pharmacological and genetic perturbations in mouse melanoma models. Ultimately, I expect to identify age-specific metabolic adaptations in pre-metastatic niches and to document that these pathways provide targets for personalized therapy of metastatic tumors in the elderly. This ambitious proposal is based on my expertise in elucidating the instructive role of metabolites in cell fate and function, and on expert skills in cancer metabolism research that I will acquire in the Fendt lab.

Keywords: metastasis,nutrient priming,melanoma
EOinTime: Satellite-based change detection and predictive monitoring of infrastructure grids based on high-resolution data (EOinTime)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Other destinations
Action type: EIC actions
Countries involved: Germany
Call: EIC Accelerator 2024 - Short application (HORIZON-EIC-2024-ACCELERATOR-01, 2024)
Duration (months): 36.0
Total project cost (EUR): 15000000.0
EU funding (EUR): 15000000.0
Participants: 1
Project status: SIGNED

Thanks to the funding from the EIC Accelerator, LiveEO offers a holistic monitoring service for infrastructure networks based on satellite data and machine learning algorithms to identify external threats to the grid and predict future impacts by adding time series analytics to our services. The service enables rapid change detection (e.g. storm monitoring) to quickly assess the location and extent of damage and slow change detection to facilitate the prediction of potential risks. We make use of optical and radar data to offer services in real time, regardless of weather. Achieving these capabilities required the development of AI-based change detection algorithms that evaluate data at different points in time i.e. time-series and are able to detect patterns and abnormalities in the data with high precision. To do this automatically, we developed an automated process chain, able to transform data into actionable insights for our customers. Insights are available to them via mobile and web apps. This is an Equity Only proposal to complement the previous EOinTime Grant Only project, which started in October 2022 and is poised to end in March 2024.

Keywords: machine learning,artificial intelligence,infrastructure,synthetic aperture radar,preemptive maintenance
Material Decomposition in X-ray Phase-Contrast Imaging with Coherent Sources (DEPICT)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Condensed Matter Physics
Panel description: Structure, electronic properties, fluids, nanosciences, biological physics
Countries involved: Germany
Call: ERC CONSOLIDATOR GRANTS (ERC-2023-COG, 2023)
Start date: 2024-03-01
End date: 2029-02-28
Duration (months): 60.0
Total project cost (EUR): 2620750.0
EU funding (EUR): 2620750.0
Participants: 1
Project status: SIGNED

X-ray microtomography is increasingly popular at synchrotron sources worldwide. The high flux and excellent beam properties allow for a fast three-dimensional visualization of various kinds of specimens at micrometre spatial resolution and exceptionally high image quality. But all these imaging instruments use the unique synchrotron sources merely to depict the objects’ internal structure. All further quantitative information encoded into the data remains unused. The reason is a lack of an image retrieval method, which accurately models the whole image formation process with highly-coherent radiation on the micrometre scale to extract all contained information from a single exposure. Consequently, these microtomographic images always contain a mixture of different signals based on various interaction effects – namely attenuation, refraction and scattering. Accurately separating all the signals will allow to quantify concentration and microstructure of materials of interest and to locate them inside the object, which is unattainable in a single exposure at the moment. The main goal of this project is to enable simple and fast X-ray microtomography at highly-coherent sources to fully exploit the disregarded information contained in the imaging data. My team will develop a robust algorithm for accurate signal separation modelling all interaction effects and demonstrate the benefit of material decomposition for highlighted applications. With this tool at hand our ambition is to fundamentally change the way of imaging at modern synchrotron sources: Imaging will no longer be solely a morphological method, as quantitative and material-specific information will become standardly available in fast microtomography at all coherent sources worldwide.

Keywords: quantitative imaging,material decomposition,coherent sources,phase retrieval
HyperPV, the first GPU-Powered Physical Verification Framework with High Performance Computing capabilities. (hyperPV)

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: Romania
Call: EIC Accelerator Open (HORIZON-EIC-2022-ACCELERATOR-01, 2022)
Start date: 2023-01-02
End date: 2025-07-01
Duration (months): 30.0
Total project cost (EUR): 3698758.75
EU funding (EUR): 2500000.0
Participants: 1
Project status: SIGNED

Electronic Design Automation industry has been a crucial driving force behind the growth of the Semiconductor industry. Nowadays chips have extremely complex designs. As they need to be verified and tested before the manufacturing, to avoid huge re-spin costs and additional expensive engineer hours, EDA companies deliver tools assisting semiconductor companies in addressing complex design automation challenges. Currently, EDA is not able to cope with the ever-evolving semiconductor industry and the need to boost the calculus capacities. In particular, current EDA tools, based on CPU technologies, are energy-inefficient, expensive and take long times for results. To this extent, AMSIMCEL is developing HyperPV, the first GPU-Powered Physical Verification Framework based on a High Performance Computing Design Rule Checking engine which will disrupt the EDA market by solving current bottlenecks of the design lifecycle. HyperPV can be accessed through a SaaS cloud platform.

Keywords: semiconductors,physical design verification pv,design rule checking drc,high performance computing hpc,electronic design automation eda,general purpose graphic processor units gpgpus,chips,saas cloud platform