Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Upscaling of blue elementary emitter to revolutionize OLED manufacturing (beeUP)

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: Germany
Call: EIC Accelerator Open 2025 (HORIZON-EIC-2025-ACCELERATOR-02, 2025)
Start date: 2025-10-01
End date: 2027-09-30
Duration (months): 24.0
Total project cost (EUR): 3571187.5
EU funding (EUR): 2499831.25
Participants: 1
Project status: SIGNED

In OLED, blue has been the critical weak point ever since the commercialization of OLED displays. Because of the inefficient blue emitters still used today, OLED TVs receive poor energy star ratings and smartphones run out of battery quickly. Additionally, their poor stability results in “burn in”, which, for example, requires smart watches to be dimmed down most of the time. Market penetration of OLED in TV is therefore stuck at 3-5%. Given the importance of blue emitters for the OLED market (and the resulting commercial attractiveness), numerous attempts have been made in the last 20 years to solve this problem. However, neither startups nor large corporates have been able to present a solution viable for high-volume manufacturing. Especially scaling up a promising material from laboratory scale to mass production is still a major hurdle for most solutions. beeOLED is solving the last big problem of the OLED industry - a highly efficient, long lasting, deep-blue emitter. Building on 20 years of OLED industry experience, the founding team of beeOLED has rediscovered an almost forgotten star amongst the display materials: divalent Europium (Eu2+). Leveraging the latest advances in Europium chemistry, beeOLED managed, for the first time ever, to stabilize Eu2+ such that it becomes usable in OLEDs. The fundamental concept is protected by several base patents, solely owned by beeOLED. beeOLED’s technology enables OLED displays with significantly improved efficiency, brightness and chemical stability at lower overall costs due to a less complex device structure. The overall OLED materials market is valued today at US$1.7bn/y, with a CAGR of 15%. Initially, beeOLED will focus on the OLED emitter dopant materials, which is a highly promising market with strong profit margins. After successfully scaling up the production, the goal of beeOLED is to achieve a dominant position in this market with the vision to become the No .1 OLED emitter material provider in 2030.

Keywords: oled,emitter,material,lanthanide,lanthanides,display,lifetime,europium,cerium,electronics
COSMOlogy employing MAchine Learning Techniques and Advanced statistics (COSMOMALTA)

Framework: Horizon Europe — Widening Participation and Strengthening the European Research Area
Programme: Widening participation and spreading excellence
Theme: Attracting and mobilising the best talents
Destination: Attracting and mobilising the best talents
Action type: Marie Skłodowska-Curie actions
Panel: Physics
Countries involved: Malta
Call: ERA Fellowships (HORIZON-WIDERA-2023-TALENTS-02, 2023)
Start date: 2025-05-01
End date: 2027-04-30
Duration (months): 24.0
Total project cost (EUR): 161411.52
EU funding (EUR): 161411.52
Participants: 1
Project status: CLOSED

Some of the biggest open problems in modern cosmology are the nature of the cosmic dark sector, the discrepancy between the theoretically predicted versus the observed value of the cosmological constant, and the growing cosmological discordances and tensions between different observational probes. Notably, the Hubble constant, which describes how fast the Universe is expanding when measured locally, has an enormous statistical disagreement with that inferred from the early Cosmic Microwave Background data. These inconsistencies, in turn, necessitate the formulation of new physics beyond the standard cosmological model. Current and ongoing observations, together with upcoming surveys, will produce large volumes of data, whose accumulation and processing will require an upgradation and increase in the sophistication of our statistical tools before applying them to specific problems. Thus, we propose to build a deep learning architecture using advanced statistics in machine learning algorithms like neural networks to be integrated into cosmological community codes for emulated parameter inference. This will help us to select, in a model-independent way, generic features of some cosmological theories that satisfy all observations. Utilising the power of deep learning will be an ideal space to investigate new physics in the observational sector and discriminate between models that are degenerate in terms of current observational approaches, fostering the development of data-driven science as a valuable companion to the model-driven paradigm. The fellowship will contribute to the researcher's career development by acquiring advanced skills in machine learning approaches using Bayesian statistics and developing skills within the cosmological community through a series of events designed to disseminate his results to the broader public. The project will also serve to consolidate and extend the researcher's network of professional contacts within Europe and beyond.

Keywords: cosmological tensions,modified gravity,reconstruction,machine learning,advanced statistics,forecasting
Decoding the transcriptomic programs that define human macrophage function. (MACROCODE)

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: United Kingdom
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2025-06-02
End date: 2027-06-01
Duration (months): 24.0
Total project cost (EUR): 260347.92
EU funding (EUR): 260347.92
Participants: 1
Project status: SIGNED

Macrophages are essential cells of the innate immune system, playing crucial roles in coordinating inflammatory responses and maintaining homeostasis. In cases of chronic inflammation, damaged tissues release abnormal signals that attract macrophages precursors to the local environment and drive macrophages toward a proinflammatory phenotype. This creates a feedback loop that sustains inflammation. The consequences to society are huge: chronic inflammatory diseases are among the leading causes of death and disability worldwide. Recent single-cell multi-omics studies have identified dysregulated macrophages as central players in chronic inflammation, but the molecular mechanisms sustaining these responses remain poorly understood. MACROCODE aims to unravel the molecular mechanisms governing the altered function of macrophages during chronic inflammation. The specific aims of this project are: 1. Use CRISPR-Cas9 technology with single-cell read-outs to perturb genes in macrophages that show dysregulated expression between healthy and diseased states in chronic inflammatory conditions . Genes will be selected by creating an integrated single-cell transcriptomics atlas of macrophages in both health and disease. 2. Develop a computational framework using bioinformatics and AI tools to link these perturbed genes to transcriptomic programs, cellular functions and in vivo disease phenotypes. 3. Validate key genes by modulating their expression using dCas9-VPR or KRAB systems, and evaluate the effects on macrophage differentiation, identity and function. Altogether, MACROCODE will decode the genetic regulation of macrophage dysfunction in chronic inflammation. This multidisciplinary project will equip me with advanced skills in genomics and bioinformatics, enhance my leadership and transferable skills, and prepare me for a future as an independent leader in the field of gene therapy for immune diseases.

Keywords: macrophages perturbational screening single cell transcriptomics chronic inflammation
DIGItal innovation and data technology network for RANGELAND livestock farming systems (DIGI-Rangeland)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Food, Bioeconomy Natural Resources, Agriculture and Environment
Theme: Bio-Environment
Destination: Innovative governance, environmental observations and digital solutions in support of the Green Deal
Action type: Coordination and support actions
Countries involved: France,Romania,Greece,Spain,Croatia,Norway,United Kingdom,Switzerland,Slovenia,Bulgaria,Iceland
Call: Thematic networks to compile and share knowledge ready for practice (HORIZON-CL6-2024-GOVERNANCE-01, 2024)
Start date: 2025-02-01
End date: 2029-01-31
Duration (months): 48.0
Total project cost (EUR): 2999999.51
EU funding (EUR): 2999999.51
Participants: 13
Project status: SIGNED

Rangeland livestock farming systems (RLFS), despite facing challenges, play a significant role in achieving the sustainable development goals in Europe. Although RLFS have an important role in the transition towards the European Green Deal, with the provision of crucial ecosystem services, they still require support, including extension services, farmer to farmer network and participatory monitoring and evaluation. The use of innovative technologies has an important role to play and take part of a wider bundle of measures required to improve the future viability of such farming systems. DIGI-Rangeland network aims to create and expand an innovative network about digital innovations and data technologies with actors (farmers and other land-users) facing challenges in rangeland territories at regional, national, and European levels. Based on a multi-actor approach, the project aims to increase knowledge sharing and mutual understanding between EU stakeholders, and foster cross-fertilization between rangeland areas actors around the use of digital technologies and innovations (DTI). To achieve this, partners in 10 countries will be involved in: a) setting up an EU network with farmers groups, land-users, 10 innovation and demonstration HUBs, and important stakeholders in the 10 rural-AKIS; b) identifying, prioritising, and analysing needs, obstacles and expectations from farmers and land-users; c) identifying and analysing DTI solutions matching the needs of the RLFS farmers and other land-users; d) evaluating and assessing solutions and barriers, to foster DTI adoption within RFLS; e) improving knowledge level with a range of scaling activities, demonstrations, and training, for farmers, advisors, students, land-users. Partners will prepare and implement communication and dissemination activities through trusted channels to maximise the impact of the project’s outputs.

Keywords: digital technologies,extensive livestock farming systems,rangelands
Deep-sea carbonates under pressure: mechanisms of dissolution and climate feedbacks (Deep-C)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Earth System Science
Panel description: Physical geography, geo logy, geophysics, atmospheric sciences, oceanography, climatology, cryology, ecology, global environmental change, biogeochemical cycles, natural resources management
Countries involved: France
Call: ERC STARTING GRANTS (ERC-2024-STG, 2024)
Start date: 2025-01-01
End date: 2029-12-31
Duration (months): 60.0
Total project cost (EUR): 2278468.0
EU funding (EUR): 2278467.75
Participants: 1
Project status: SIGNED

As humanity grapples with the escalating crisis of climate change, understanding and mitigating the sources of carbon dioxide (CO2) emissions is imperative. A lesser-known yet significant aspect of this crisis lies in the vast expanses of marine sediments which encompass two-thirds of the Earth’s surface. A key component of these sediments is calcium carbonate (CaCO3), a family of minerals that makes up the shells and skeletons of marine organisms. CO2 emissions lead to ocean acidification, triggering CaCO3 dissolution, which in turn neutralizes CO2, acting as a crucial CO2 sink over millennial timescales. However, the mechanisms and rate of this dissolution remain unknown due to the challenges posed by the deep ocean’s high pressures and the elusive bacterial communities mediating the dissolution process. Proposed here is an ambitious five-year research initiative aimed at understanding deep-sea CaCO3 dissolution, thereby paving the way towards a more thorough understanding and potential mitigation of climate change impacts. By focusing on the abyssal and hadal realms, this groundbreaking research seeks to unveil the nature and rate of CaCO3 dissolution through high pressure reactors. These reactors, which mimic the pressure and temperature of deep-sea environments, present a robust alternative to in-field studies. With the integration of cutting-edge sensors and the use of advanced CaCO3 imaging techniques, we anticipate generating precise and continuous data on the unfolding biogeochemical processes. By housing bacterial cultures within the reactors, alongside natural CaCO3 grains, this project will delve into the mechanisms driving dissolution. The insights will be instrumental in refining a global biogeochemical model, thereby promoting a deeper comprehension of the ocean’s role in carbon sequestration and propelling forward the global efforts towards effective climate change mitigation.

Keywords: calcite,aragonite,pressure,solubility,carbonate,calcium,carbon,sediment,ocean,thermodynamics,kinetics,dissolution,degradation,organic,microbes,bacteria,dna,alkalinity,acidification,climate,change,mitigation,co2,reactor,laboratory,model
Quantifying Evolution: a platform for the synthetic evolution of molecular ecosystems. (Q-EVO)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Condensed Matter Physics
Panel description: Structure, electronic properties, fluids, nanosciences, biological physics
Countries involved: Italy
Call: ERC ADVANCED GRANTS (ERC-2024-ADG, 2024)
Start date: 2026-02-01
End date: 2031-01-31
Duration (months): 60.0
Total project cost (EUR): 2435177.0
EU funding (EUR): 2435177.0
Participants: 1
Project status: SIGNED

Among the fundamental processes governing Nature, evolution is one of the most thoroughlAmong the fundamental processes governing Nature, evolution is one of the most thoroughly described and less formalized. The grand goal of describing evolution via clean universal laws is made difficult by the inherent complexity of both the biological systems and the available experimental models, which generally involve a large number of potentially relevant variables of difficult quantification. This project is built around the Affinity-based DNA Synthetic Evolution (ADSE) platform, an experimental strategy to generate and study evolution at a molecular level that enables full quantitative description of the eco-evolutionary processes. ADSE is inspired by the in-vitro molecular selection protocol “SELEX” and enables observing the evolution across generations of large populations of DNA oligomers whose identity is given by their sequence. The key innovation is that here evolutionary mechanisms and ecological interactions (selection of the fittest, resource gathering capacity, parasitism, mutualism etc…) are given by molecular interactions based on DNA-DNA hybridization, a process that is well known and predictable from sequences. Q-EVO is based on a combination of ADSE with an innovative array-based biosensor, approaches of data science and notions of theoretical ecology. Q-EVO aims at providing a (still missing) quantitative description of the key concepts of fitness and speciation, and of the relationship between a given environment and the evolution of its ecosystem. I propose a program capable to produce hundreds of parallel evolutions for tens of generations exploring a vast variety of conditions. Raw data, formed by hundreds of millions of reads, will be analysed to follow population dynamics, extract phylogenetic paths, parametrizing the network of ecological interactions (e.g. competitors, mutualists, parasites) and will be made public in a repository created ad hoc.

Keywords: molecular model of eco evolution,dna nanotechnology,molecular interactions
Exploring Novel Drivers of Anti-Microbial Resistance (ENDAMR)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Doctoral Networks
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: Germany,Sweden,Italy,Norway,Hungary,Israel,Switzerland,Spain,Portugal
Call: MSCA Doctoral Networks 2023 (HORIZON-MSCA-2023-DN-01, 2023)
Start date: 2025-01-01
End date: 2028-12-31
Duration (months): 48.0
Total project cost (EUR): 2669407.2
EU funding (EUR): 2669407.2
Participants: 15
Project status: SIGNED

Antibiotic resistance (AMR) is a major public health issue, with 5 million deaths in 2019 linked to AMR worldwide. These numbers are comparable to the toll of the SARS-CoV-2 pandemic. Without new solutions, AMR is projected to soon become one of the leading causes of death in the EU. Addressing this challenge requires the development of new, effective antibiotics, but this alone is not sufficient due to the rapid evolution of bacteria. Understanding the drivers and mechanisms of AMR is vital to delay or reverse resistance in both existing and new antibiotics, especially since no new broad-spectrum antibiotics have been developed since the 1990s and their development is a lengthy process with high attrition rates. The ENDAMR doctoral network aims to better equip researchers in Europe to understand and develop new strategies to tackle AMR. WP1 focuses on how AMR affects the fitness of pathogenic bacteria in the gut microbiome, aiming to identify microbiome characteristics that predispose to AMR infections and to explore microbiome-based interventions. WP2 examines AMR acquisition via horizontal gene transfer, investigating evolutionary pathways, host genetics, environmental factors, dissemination, and AMR reservoirs. WP3 is dedicated to understanding the frequency, mechanisms, and clinical implications of antibiotic resistance, with a particular focus on the less studied aspects of heteroresistance and tolerance, and to developing diagnostic tools and predictive models. WP4 explores the combination of antibiotics to enhance treatment outcomes and potentially prevent or reverse AMR, based on understanding the interplay of resistance mechanisms. ENDAMR will also prepare doctoral candidates for various career paths beyond academia, including teaching, science communication, and entrepreneurship. Candidates will gain transferable skills and learn from industry role models, equipping them to make significant contributions to solving the AMR crisis.

Keywords: antimicrobial resistance heteroresistance cross resistance collateral sensitivity microbiome horizontal gene transfer
Trustworthy Artificial Intelligence for Personalised Risk Assessment in Chronic Heart Failure (AI4HF)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Health
Theme: Health
Destination: Staying Healthy in a rapidly changing society
Action type: Research and Innovation actions
Countries involved: Netherlands,Spain,Portugal,Germany,France,Belgium,United Kingdom,Greece,Türkiye,Czechia,Tanzania, United Republic of,Peru
Call: Trustworthy artificial intelligence (AI) tools to predict the risk of chronic non-communicable diseases and/or their progression (HORIZON-HLTH-2022-STAYHLTH-01-two-stage, 2022)
Start date: 2023-06-01
End date: 2027-05-31
Duration (months): 48.0
Total project cost (EUR): 5910451.25
EU funding (EUR): 5910451.25
Participants: 17
Project status: SIGNED

Cardiovascular diseases remain the main cause of mortality worldwide; in particular, heart failure (HF) poses complex challenges in clinical practice, as it is associated with a significant variability in aetiologies, manifestations and risks, as well as in its progression and trajectories over time. Clinical risks of HF can vary from reduced cardiac function and regular hospitalisations, all the way to cardiac events and mortality. There is a need for a personalised medicine approach to tailor the care models (i.e. lifestyle changes, medications, interventions) to each HF patient’s risk profile and hence optimise the clinical outcomes. Artificial intelligence (AI) solutions trained from multi-source cardiovascular data have the potential to dissect the precise characteristics of each patient and predict their likely trajectories at an early stage. However, existing AI methods remain a far distance from clinical transfer and adoption due to a common and key limitation: their trustworthiness and acceptance by cardiologists and patients alike have not been achieved. AI4HF will develop the first trustworthy AI solutions for personalised risk assessment and management of HF patients. The project will build on a unique set of big data repositories, trustworthy AI methods, computational tools and clinical results from major EU-funded projects in cardiology. To test robustness, fairness, transparency, usability and transferability, the validation with take place in eight clinical centres in both high- and low-to-middle-income countries in the EU and internationally. AI4HF will develop a comprehensive and standardised methodological framework for trustworthy and ethical AI development and evaluation based on the FUTURE-AI guidelines developed by the consortium members. AI4HF will be implemented through continuous multi-stakeholder engagement, taking into account clinical needs and patient preferences, as well as socio-ethical and regulatory perspectives.

Keywords: heart failure,personalised risk assessment,trustworthy ai,multi stakeholder engagement,multi faceted evaluation,ai regulation