Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Brain-inspired Resistive Artificial Ionic Neural Networks based on Crossbar-arrays of Conic Channels (BRAIN-CCC)

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: Netherlands,Korea, Republic of
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2026-01-15
End date: 2028-01-14
Duration (months): 24.0
Total project cost (EUR): 217076.16
EU funding (EUR): 217076.16
Participants: 2
Project status: SIGNED

The energy consumption of machine learning (ML) is doubling every 2 months, outpacing global energy production within the next decade. Neuromorphic computing, in particular, memristive crossbar arrays have shown energy reductions of 2 orders of magnitude in ML. However, the training is typically performed on conventional computers, leading to significant energy losses. Conical microfluidic channels have shown promise as volatile memristors, and there has been early progress toward achieving nonvolatile behavior in these channels. However, the conductance of the channels can only be increased and not decreased, which is crucial for ML. In BRAIN-CCC I will study the interactions of chemically functionalized surface groups, chemical shocks, pressure gradients and electric impulses in simulations to achieve reversible nonvolatile dynamics of the conductivity in these channels. From this, I will develop a model for the conductivity dynamics. To test the feasibility of performing ML training directly in hardware, without relying on conventional computers, I will first design and assemble a prototype using variable resistors. Subsequently, I will replace the variable resistors with iontronic channels and measure the real world advantages of this approach, continuing a collaboration with an experimental group. BRAIN-CCC will be conducted at Utrecht University. Knowledge transfer in BRAIN-CCC will be bidirectional: The host contributes expertise in soft matter systems and ML, while I provide knowledge of designing and building electronic hardware and interdisciplinary knowledge transfer. The combination of these skills is crucial to realizing a prototype of ML hardware, based on iontronic channels. The project combines interdisciplinary approaches from physical chemistry, electrodynamics, electrical engineering, and computer science, to realize significant gains in energy efficiency, addressing one of the most pressing issues facing the EU and the world: the climate crisis.

Keywords: iontronics,electrolytes,neuromorphic computing,machine learning
INCREASING LUTETIUM PRODUCTION, WHILE LEVERAGING METABOLIC IMAGING TO ENHANCE THERANOSTICS EFFECTIVENESS (ILLUMINATE)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Health
Theme: Public-private partnerships
Destination: Innovative Health Initiative
Action type: Research and Innovation actions
Partnership: IHI (JU-JTI)
Countries involved: Netherlands,Finland,Germany,Italy,Sweden
Call: Development and proof of principle of new clinical applications of theranostics solutions (HORIZON-JU-IHI-2023-05, 2023)
Start date: 2024-10-01
End date: 2029-03-31
Duration (months): 54.0
Total project cost (EUR): 19077451.25
EU funding (EUR): 11589148.25
Participants: 16
Project status: SIGNED

Selective targeting of therapy with antibody-radioisotope combinations have led to major advances in treating cancers that are resistant to common therapies, such as castrate-resistant prostate cancer. Adding Lutetium-177-PSMA (Lu-177-PSMA) to standard care significantly prolongs survival. However, the increased use of Lu-177-based therapies has resulted in an increased demand for this isotope, leading to shortage risks.In addition, despite the success of Lu-177-treatment, ~30% of patients do not benefit from treatment, but do suffer from side-effects. ILLUMINATE, will improve the availability and use of theranostics across its value chain by taking a hybrid approach: 1) Imaging capabilities will be improved by implementing novel, broadly applicable, scalable and highly effective Metabolic Magnetic Resonance Imaging (MeMRI), which are expected to give better and faster insight into treatment responses, allowing better patient stratification and treatment personalisation; 2) The availability of Lu-177 will be fortified by improving its manufacturing process, and by developing sustainable production methods to help meet the general need for radio-isotope based therapies and increase EU autonomy. The ILLUMINATE consortium (industries Philips, NRG, Bracco, TeslaDC, Wavetronica, Lygature, SDN, EuroBioImaging, and the Univerisities of Torino, Lund, Amsterdam UMC, Klinik Essen, coordinated by UMC Utrecht) uses the example of Lu-177-PSMA as a showcase clinical trial in 150 patients for broadly applicable innovative technologies in the field of theranostics and radioisotope manufacturing for oncology.

Keywords: metabolic mri,oncology,prostate cancer,theranostics,medicinal radionucleotide production
ST-001 - Transforming pancreatic cancer therapy through novel drug-loaded ultrasound-targeted microbubbles (SONOTARG)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Other destinations
Action type: EIC actions
Countries involved: United Kingdom
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

SonoTarg Ltd aims to transform cancer therapy with an innovative ultrasound-responsive microbubble platform. Founded in 2018, SonoTarg builds on the research of Professors Callan, McHale (Ulster University), and Prof. Stride (University of Oxford) into the combined potential of sonodynamic therapy (SDT) and microbubbles. This technology integrates SDT with chemotherapy, offering precise, targeted delivery of therapeutic agents to cancerous tissues to minimize systemic toxicity—a critical advancement for pancreatic cancer treatment. Pancreatic cancer is among the deadliest cancers globally with a 98% loss of healthy life expectancy once diagnosed, limited treatment options and poor survival rates (3% beyond 5 years). SonoTarg addresses this challenge with significantly enhanced efficacy in preclinical models, achieving 2~3 times greater tumour reduction using 10–20% of the standard clinical chemotherapy dose. This translates to enhanced treatment efficacy with fewer side effects—improving patient quality of life, compliance and eligibility for surgery. The company secured €2.65m in pre-seed funding and grants, filed 5 patents (2 granted), with an identified regulatory approvals pathway from the MHRA, including an "Innovation Passport" award. This underscores SonoTarg's readiness to advance ST-001 into clinical trials starting Q1/Q2 2025 at Belfast Health & Social Care Trust, UK, for safety and efficacy validation in humans. SonoTarg seeks €2.5m in EIC Accelerator grant-only funding for finalizing ST-001 development, conducting clinical trials, and securing regulatory approval within 24 months. This funding will refine manufacturing processes and clinical protocols, and position SonoTarg as a leader in ultrasound-mediated cancer therapies. SonoTarg’s strategy aligns with the ambitions of the EU Sustainable Development Goals and aims to revolutionize cancer treatment, improve chemotherapy efficacy and patient outcomes, and capture oncology market share.
Hydrogen from Seawater Electrolysis (HySEas)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Public-private partnerships
Destination: Clean Hydrogen
Action type: Research and Innovation actions
Partnership: CLEANH2 (JU-JTI)
Countries involved: Greece,United Kingdom,Norway,Portugal,Slovenia,Spain
Call: Development of innovative technologies for direct seawater electrolysis (HORIZON-JTI-CLEANH2-2024, 2024)
Start date: 2025-05-01
End date: 2028-04-30
Duration (months): 36.0
Total project cost (EUR): 3999970.85
EU funding (EUR): 3999970.85
Participants: 8
Project status: SIGNED

HySEas introduces an innovative approach to direct seawater electrolysis through the integration of a Bipolar Membrane (BPM), incorporating both a cation exchange layer akin to a proton exchange membrane (PEM) and an anion exchange layer similar to anion exchange membrane (AEM) as well as a water dissociation catalyst layer, within a Bipolar Membrane Water Electrolyser (BPMWE) device. Thus far, BPMWEs have been tested using conventional borrowed components from PEM and AEM water electrolysers. These tests have revealed limitations in terms of both durability and overall performance. Here, the development of the BPMWE is combined with the recovery/reuse of salts and compounds from seawater brine through the development of innovative technological solutions: a) metal ion removal from seawater through direct reduction/photoreduction on oxide particles; and b) oxide particle regeneration through acid treatment to capture and reuse metals. Moreover, the drastic reduction of CRM will be achieved by: a) conducting a mitigation strategy to reduce Ir loading: sputter deposition on powder substrates to deposit the conductive layer of various morphologies between iridium catalyst and TiO2 support; b) using Fe, Ni, Mo oxide catalysts and other high entropy compounds to replace Ir-based catalysts; and c) using metal phosphides, sulphides and carbides (CoP, MoS2, Mo2C, etc.) to replace Pt-based catalysts. Plasma treatment is used to engineer defects and vacancies and increase the active site density on the catalyst surface as well as to promote transport by manipulating particle size and enhanced pore distribution. HySEas also deploys customized computational models via multiphysics simulations, integrating microkinetic and microscopic models into a novel multiscale model of a salty water electrolyser. Finally, extensive stack assessment is conducted in terms of performance and durability.

Keywords: bipolar membrane plasma treatment critical raw materials multiphysics simulation microscope models electrolysis seawater
Political Polarisation and Communist Past: Czech and German Case (POPOLACOP)

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: Germany
Call: MSCA Postdoctoral Fellowships 2022 (HORIZON-MSCA-2022-PF-01, 2022)
Start date: 2024-01-01
End date: 2025-12-31
Duration (months): 24.0
Total project cost (EUR): 94843.68
EU funding (EUR): 94843.68
Participants: 1
Project status: SIGNED

Political polarization, populism and illiberal tendencies in post-communist countries stem among other sources from their communist heritage, whose bearers are, paradoxically, also organizations of political prisoners who unintentionally transformed the political polarisation characteristic of the socialist dictatorship into a strong current of anti-communism and a polarized view of history. The research objectives are to: (1) study the roots of the narrative of polarisation among the politically persecuted; (2) analyse the value system, the formation of values and their transfer within associations of political prisoners; (3) study the transfer of their polarised narrative and value system to society and the degree of their success. Although there is a large body of literature focusing on the politics of memory, survivor trauma and anti-communism, none of them has addressed the link between political polarization and coping with the communist past. The project combines the historical research methodology of data gathering with the concepts of psychology (political polarisation and narrative). The German-Czech comparative project is expected to: (1) contribute to the emerging literature on political polarisation; (2) bring a better understanding of the links between the success of the democratization process and radical forms of anti-communism and populism while contributing to the ever growing debate on the need to use a non-polarized approach towards the communist past in education, politics and history; (3) initiate discussions on the role of political bodies with respect to victim organisations and their members and the necessity of various forms of help for persecuted persons (financial, psychological etc.). The project seeks to transfer best practices from Germany, where a more balanced view of the past seems to be prevalent, and analyse which historical, cultural, social and political factors have contributed to it.

Keywords: political polarisation organisation of political prisoners populism illiberal democracy political prisoner anti communism,czech republic,germany,20th century
Co-designing human centric pathways for future skills in manufacturing through augmented, empowered, inclusive, and symbiotic complementarities between AI, automation and human task (SKillAIbility)

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: Italy,Norway,Greece,Sweden,Netherlands,Spain,Belgium,Switzerland,Latvia
Call: Assessing and strengthening the complementarity between new technologies and human skills (HORIZON-CL2-2024-TRANSFORMATIONS-01, 2024)
Start date: 2024-12-01
End date: 2027-11-30
Duration (months): 36.0
Total project cost (EUR): 2999000.0
EU funding (EUR): 2999000.0
Participants: 14
Project status: SIGNED

The modern manufacturing landscape is undergoing a transformative twin transition, integrating green and digital technologies to bolster value chain resilience and explore re-shoring options. Amidst this paradigm shift, concerns about job displacement by machines, particularly through the rise of Artificial Intelligence (AI) and Machine Learning (ML), have become more pronounced. This proposal delves into the challenges posed by the pursuit of excellence in the Industry 5.0 framework, focusing on the potential impact of digital technologies on job nature and the ensuing need for human-technology complementarity. The potential for AI to exacerbate social disparities and inequalities, especially for vulnerable groups, is also a significant concern. Additionally, the manufacturing sector faces labor shortages, impacting innovation capacity and economic competitiveness. The mission of SKillAIbility is to address these challenges, emphasizing the need for a human-centric approach to assess digital technologies and enhance workers' employability. The proposal also outlines the short, medium, and long-term contributions of SKillAIbility towards a resilient, inclusive, digital transition in the manufacturing industry. This initiative provides tools and methodologies to understand and respond to the impacts of emerging digital technology advancements on human tasks, skills, training, and policymaking. SKillAIbility's holistic impact spans various societal, industrial, academic, and regulatory dimensions, affecting citizens, people with disabilities, workers, trade unions, industry players, research institutions, and governmental bodies. Through upskilling and reskilling initiatives, SKillAIbility aims to empower citizens and workers, mitigate the risk of task automatization, and increase employability within the advanced manufacturing sector. Furthermore, the proposal contributes to the manufacturing industry's growth by filling skills gaps, increasing performance and pro

Keywords: manufacturing learning factory digital technologies skills human
C-CLAW - The revolutionary Non-intrusive mechanical Fastener that facilitates life extension of existing infrastructures (C_CLAW_C_HAWK)

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: France
Call: EIC Accelerator Open 2021 (HORIZON-EIC-2021-ACCELERATOROPEN-01, 2021)
Start date: 2022-03-01
End date: 2024-10-31
Duration (months): 32.0
Total project cost (EUR): 3568592.5
EU funding (EUR): 2498014.75
Participants: 1
Project status: CLOSED

Civil Engineering contributes to ~15% of CO2 emissions. This is where Cold Pad brings a gamechanger with reliable and durable composite bonding & fastening solutions. After revolutionizing structural adhesive bonding techniques for the marine sector, we now ambition to democratize our non-intrusive solutions for harsh industrial environments, Civil engineering and the Hydrogen value chain. Our composite bonding & fastening solutions facilitate structural works from the construction phase through life extension of large industrial structures, either made of steel or concrete, and with neither business interruption nor risk of explosion. It is a true alternative to welding or drilling. Our unique selling points allow to solve the difficult equation of making the life extensions of mature industrial infrastructures both environmentally and economically viable which resonates with the green deal objective of Building and renovating in an energy and resource efficient way.

Keywords: structural maintenance,atex,no hot works,deeptech non intrusive,adhesive bonding,composites,concrete,renovating steel,asset integrity,asset integrity,mechanical fastening,,reliable,hydrogen
Deciphering the role of surface mechanics during cell division (MitoMeChAnics)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Cell Biology, Development, Stem Cells and R egeneration
Panel description: For all organisms : Structure and function of the cell, cell -cell communication, embryogenesis, tissue differentiation, organogenesis, growth, development, evolution of development, organoids, stem cells, regeneration, therapeutic approaches
Countries involved: Germany
Call: ERC CONSOLIDATOR GRANTS (ERC-2023-COG, 2023)
Start date: 2024-04-01
End date: 2029-03-31
Duration (months): 60.0
Total project cost (EUR): 2200287.0
EU funding (EUR): 2200287.0
Participants: 1
Project status: SIGNED

Cell division is fundamental for all life forms and its dysregulation can lead to severe diseases including cancer. The dramatic shape changes eukaryotic cells have to undergo to divide are mostly driven by the cell surface, a complex system that can dynamically modify its mechanical properties. While the importance of physical cues for cell division has long been recognized, the lack of specific tools that modify particular physical properties and bridge molecular-to-cellular scale biophysics prevent us from understanding how cells use their mechanics to regulate form and function. To address this, my group has recently developed a new class of molecular tools that for the first time allows us to manipulate surface mechanics specifically and acutely in living cells. Exploiting this powerful new structural biophysics approach, our first discoveries excitingly demonstrate that the strength of tethering between the plasma membrane and the cortical cytoskeleton is a key control mechanism for cell division, both in cultured cells as well as in mouse embryos. The overarching goal of MitoMeChAnics is to systematically understand how cell surface mechanics controls the different steps of division. My team and I will systematically and quantitatively link cell surface architecture with the resulting mechanics and morphology to determine the structure-function relationship at the cellular periphery in space and time. To this end, we will deploy novel molecular tools and combine them with cellular biophysical measurements, super resolution microscopy and in situ cryo-electron tomography. Moreover, we will build data-driven theoretical models to unravel the physical principles that control the membrane-cortex interface, and test their predictions with novel optogenetic tools. Our project thus takes a highly interdisciplinary approach, combining mechanobiology, molecular engineering, structural analysis and theory to decipher how cells their mechanics to control mitosis.

Keywords: cellular biophysics,surface mechanics,abscission,cytokinesis,molecular engineering,afm,cryo et,superresolution,theoretical modelling,mesc,fibroblasts
Optimizing for Generalization in Machine Learning (OPTGEN)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Computer Science and Informatics
Panel description: Informatics and information systems, computer science, scientific computing, intelligent systems
Countries involved: Israel
Call: ERC STARTING GRANTS (ERC-2022-STG, 2022)
Start date: 2023-10-01
End date: 2028-09-30
Duration (months): 60.0
Total project cost (EUR): 1494375.0
EU funding (EUR): 1494375.0
Participants: 1
Project status: SIGNED

Recent advances in the field of machine learning (ML) are revolutionizing an ever-growing variety of domains, ranging from statistical learning algorithms in computer vision and natural language processing all the way to reinforcement learning algorithms in autonomous driving and conversational AI. However, many of these breakthroughs demonstrate phenomena that lack explanations, and sometimes even contradict conventional wisdom. Perhaps the greatest mystery of modern ML---and arguably, one of the greatest mysteries of all of modern computer science---is the question of generalization: why do these immensely complex prediction rules successfully apply to future unseen instances? Apart from the pure scientific curiosity it stimulates, I believe that this lack of understanding poses a significant obstacle to widening the applicability of ML to critical applications, like in healthcare or autonomous driving, where the cost of error is disastrous. The broad goal of this project is to tackle the generalization mystery in the context of both statistical learning and reinforcement learning, focusing on optimization algorithms being the de facto contemporary standard in training learning models. Our methodology points out to inherent shortcomings of widely accepted viewpoints with regard to generalization of optimization-based learning algorithms, and takes a crucially different approach that targets the optimization algorithm itself; building bottom-up from fundamental and tractable optimization models, we identify intrinsic properties and develop algorithmic methodologies that enable optimization to effectively generalize in modern statistical- and reinforcement-learning scenarios. A successful outcome would not only lead to a timely and crucial shift in the way the research community approaches generalization of contemporary optimization-based ML, but it may also significantly transform the way we develop practical, efficient and reliable learning systems.

Keywords: statistical learning theory,convex optimization,reinforcement learning,generalization,online learning,regret minimization