Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Distributed Addressable Robotic Material via Wavelength-Division-Multiplexing (ColorfulAct)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Products and Processes Engineering
Panel description: Product and process design, chemical, civil, environmental, mechanical, vehicle engineering, energy processes and relevant computational methods
Countries involved: Switzerland
Call: ERC STARTING GRANTS (ERC-2025-STG, 2025)
Start date: 2026-01-01
End date: 2030-12-31
Duration (months): 60.0
Total project cost (EUR): 1499987.0
EU funding (EUR): 1499987.0
Participants: 1
Project status: SIGNED

Artificial intelligence has revolutionized today’s society by expanding machines’ intellectual ability to approach humans through the creation of advanced algorithms. The physical capability of machines, however, remains basic compared to the evolved algorithms. Natural organisms have soft distributed musculatures actively coordinated in various ways to achieve different physical functions; machines are assembled from passive and rigid components with limited functions. The field of soft robotics has taken a leap to approach organisms by employing soft elastomeric materials that can either passively adapt to the environment or actively actuated by stimulus. Development so far has mainly focused on creating unit soft actuator with muscle-like performances, yet attention to distributed and addressable actuation remains elusive. I believe distributed and addressable features are fundamental concepts to expand physical intelligence, similar to the concepts of nodes and connections are foundational for artificial intelligence. The central scientific challenge to realize distributed and addressable robotic materials lies in the narrow bandwidth of conventional stimulating signals to modulate actuation; this proposal breaks this mold by exploiting the broad bandwidth of light for high-capacity robotic materials. In this ERC proposal, I will exploit wavelength-division-multiplexing of broad band optical stimulus and establish a unified platform (i.e., materials library, actuation mechanisms, integral soft optical system) to realize distributed, addressable (i.e., individually controllable actuators at ≤ millimeter scale spatial resolution) and high capacity robotic material in a small form factor, and demonstrate advanced robotic multifunctionalities via coordination of distributed actuators in two small scale soft robot examples that are not possible with other stimuli.

Keywords: robotic materials,soft robotics,material science,optics,physical intelligence
From photons to storms: Revealing the 3D structure of giant exoplanet atmospheres (VORTEX)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Countries involved: Germany
Call: ERC CONSOLIDATOR GRANTS (ERC-2025-COG, 2025)
Start date: 2026-09-01
End date: 2031-08-31
Duration (months): 60.0
Total project cost (EUR): 1993750.0
EU funding (EUR): 1993750.0
Participants: 1
Project status: SIGNED

The solar system's giant planets, Jupiter and Saturn, host atmospheres with clear belts, cloudy bands, and storms like the Great Red Spot. They represent the class of cool, isolated giant planets. For a full view of giant planet atmospheres we must study the known exoplanet population over a wide range of orbits, temperatures, and spins. However, exoplanets are too far away to resolve them directly. The James Webb Space Telescope is changing this: time-resolved spectra probe the atmospheres of rotating wide-orbit planets, whose light curves reveal active weather. The Extremely Large Telescope's METIS and ANDES instruments will study giant planets from close to wide orbits at high spectral resolution, probing winds through Doppler shifts, allowing us to constrain flow and temperature structures. In spite of these amazing prospects, there is no framework to properly fit JWST or future ELT data. State-of-the-art techniques are prohibitively slow, preventing hypothesis testing. This is a fundamental challenge that needs to be resolved - otherwise we cannot use the information encoded in our data. With the VORTEX project I will develop the machinery to map the 3D, dynamical atmospheres of giant exoplanets, from strongly irradiated to the most isolated ones. The modeling challenge will be addressed through a feedback between novel machine learning inference methods and hydrodynamical models. Our project will answer the three questions: (i) what are the properties, scales and lifetimes of storms on isolated planets? (ii) how do flows transport gas on irradiated planets? (iii) how do atmospheres of planets on eccentric orbits react to changing irradiation? VORTEX will have a long-lasting impact by enabling the 3D interpretation of exoplanet spectra. Answers to the questions above will reveal which processes govern the 3D atmospheres of exoplanets. Our methods will pave the way for analyzing similarly complex astronomical datasets recorded with the largest telescopes.

Keywords: 3d atmospheric characterization,machine learning inference
Unwanted Invaders or Welcomed Guests: Impact of Prophage Integration Sites on Host Genomic Integrity and Evolution (PHAGE_ATTACH)

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: Life Sciences
Countries involved: Slovenia
Call: ERA Fellowships (HORIZON-WIDERA-2024-TALENTS-02, 2024)
Start date: 2025-09-01
End date: 2027-08-31
Duration (months): 24.0
Total project cost (EUR): 182717.52
EU funding (EUR): 182717.52
Participants: 1
Project status: SIGNED

Bacteriophages are found in over two-thirds of sequenced bacterial genomes, with phage DNA making up an estimated 10% of these genomes. Recent studies suggest that phage DNA integration, during so called lysogeny, can lead to interactions with bacterial hosts that are more complex than once thought. There are phages which very specifically and reversibly integrate into host genes, modulating host development. Altering bacterial behaviours can have detrimental consequences in microbial communities, such as those found in the human gut. However, it is unclear how specific phage integration mechanisms evolve, or if integration specificity influences regulation during lysogeny. I hypothesise that phage integration components are a key factor in phage-host interactions, with attachment sites (att) and phage integrases under strong evolutionary pressure. This project's goal is to examine the consequences of specific phage integration for the host and for the phage and to understand how integration components are directed by evolution. This will be fulfilled via three research objectives: 1) bioinformatics analysis of phage integration components, capitalizing on newly built prophage database and focusing on conservation of host att genes and phage integrases; 2) investigation of the effects of integrase specificity on phage-host interactions and fitness via combining engineered phages and trackable host with high-throughput imaging and RNA sequencing. 3) testing the long-term effect of specific att sites on host genome evolution in ecological context to Bacillus subtilis was selected as the model host due to its tractability, biotechnological importance, and susceptibility to phages with highly specific integrases. Understanding the impacts of phage integration specificity and its evolution will reveal new phage roles in host genome editing and gene transfer, improve our knowledge of phage-bacteria coevolution and it will pave new ways to engineering of bacterial genomes.

Keywords: phage attachment,integrase,integrase elements,bacillus,bacillus phage,lysogeny,phage host co evolution
Universal Dystrophin Expressing Chimeric (DEC) Cells Therapy for Treatment of Duchenne Muscular Dystrophy (DT-DEC01-DMD-CT)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Other destinations
Action type: EIC actions
Countries involved: Poland
Call: EIC Accelerator 2024 - Short application (HORIZON-EIC-2024-ACCELERATOR-01, 2024)
Duration (months): 48.0
Total project cost (EUR): 13750000.0
EU funding (EUR): 13750000.0
Participants: 1
Project status: SIGNED

Dystrophin Expressing Chimeric cell therapy (DT-DEC01) is a novel personalized therapy intended for treatment of Duchenne muscular dystrophy (DMD), a rare, progressive and lethal disease caused by mutations in the dystrophin gene. Currently there is no cure for DMD, thus there is an urgent need for new solutions to halt disease progression and prolong DMD patient’s survival. To respond to this need we have introduced and tested DEC in preclinical animal models of DMD and confirmed safety and efficacy of DEC evidenced by restoration of dystrophin and biodistribution of DT-DEC01 to cardiac, respiratory, and skeletal muscles, correlating with functional improvements and reduced mdx pathology. DT-DEC01 Therapy was granted EMA Orphan Drug Designation (ODD) for treatment of DMD and the Rare Pediatric Disease Designation (RPD) by the United States FDA. Recently, safety and efficacy of DT-DEC01 was confirmed in 7 DMD patients in a proof-of-concept clinical experiment (TRL5), with improvements in functional tests up to 24 months after systemic-intraosseous DT-DEC01 administration. The objective of this proposal is to advance the clinical development of DT-DEC01, as the universal, cell-based therapy available for all DMD patients regardless of gene mutation, age or ambulatory status. Dystrogen applies for blended funding to conduct Phase 1/2 clinical trial to assess safety and efficacy of DT-DEC01 therapy in DMD patients. At the end of this project, we aim to start the accelerated commercialization process by applying to EMA for conditional authorization. Introduction of a novel, universal DT-DEC-01 therapy to the armamentarium of muscular dystrophies will fulfill an urgent unmet medical need in the EU market for drugs available to all DMD patients. The unique approach of proposed personalized DT-DEC01 therapy for DMD will have significant economic impact and a tremendous effect on the quality of life of DMD patients, their caregivers as well as society.

Keywords: duchenne muscular dystrophy,musculoskeletal,rare disease,cellular therapy,dystrophin expressing chimeric dec cells
Generative AI for Climate Applications (CLIMATEGPT)

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

Erasmus AI develops ClimateGPT, a transformative AI tool powered with planetary-scale datasets to address the challenges of climate change. ClimateGPT is the first foundational Large Language Model devoted to climate change, with over 300 billion data points, and provides unparalleled detail in climate reporting and forecasting. It is equipped with large-scale corpora, 40 Natural Language Processing engines, and the world's largest independent web crawl, processing 250 million URLs daily. ClimateGPT outperforms human capabilities in processing large-scale climate datasets, integrating diverse sources such as satellite imagery and scientific publications. Its advanced ML algorithms enable precise pattern recognition, predicting complex climate patterns and trends. It offers real-time climate monitoring, analyzes multifaceted variables, and presents comprehensive insights through intuitive, user-friendly interfaces. Its speed, adaptability, and continuous learning make it invaluable for timely decision-making in critical situations. It is an invaluable tool for policy-making, risk management, and sustainable development practices across a range of sectors, including environmental agencies, emergency response teams, financial institutions, multinational corporations, research institutions, educational bodies and NGOs. Backed by an expert team and a visionary board, Erasmus AI aims to enhance collective climate social intelligence, accelerate the adoption of our solution, and pave the way towards a more sustainable future. Aligned with the UN SDGs and the European Green Deal, our technology can be instrumental in achieving climate neutrality by 2050. This proposal will allow us to complete the development of ClimateGPT, enhance its data capabilities, mitigate technological and commercial risks, scale our operations and maintain our competitive edge. It will strengthen the EU's technological sovereignty and will contribute to the global efforts towards sustainability.

Keywords: generative pre trained transformer gpt,foundational large language model llm,climate intelligence,environmental sustainability,data driven decision making,predictive analytics
RudolfVX: A multimodal foundation model for pathology to power precision medicine with human centric generative AI (RudolfVX)

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): 24.0
Total project cost (EUR): 2499999.0
EU funding (EUR): 2499999.0
Participants: 1
Project status: SIGNED

The convergence of artificial intelligence with the growing abundance of medical data has the potential to revolutionize the medical landscape. The shift to precision medicine is currently hindered by a lack of technological advancements and integration of diverse medical data. Aignostics, a spin-off from Charité Berlin, is developing RudolfVX - a large-scale multimodal foundation model for pathology, capable of leading the way to improved disease diagnosis, better therapy decisions and accelerated drug development. Emerging as the first of its kind in the EU, this sophisticated AI model is capable of creating valuable insights from multiple data types including clinical reports, omics data, and medical images. The model leverages artificial neural networks and self-supervised learning, minimizing data annotation limitations and optimizing computational efficiency. RudolfVX is set to transform pathology and healthcare by integrating generative AI solutions with a human-centric focus, pioneering new possibilities in the field. These downstream applications will stem from a transparent model, fully compliant with EU values in the AI domain. Our solution will enable improved patient outcomes, reduced healthcare costs, and will address the high failure rates in traditional drug development. With RudolfVX, Aignostics will strengthen Europe’s position and become a market leader in computational pathology, offering significant potential in precision medicine and healthcare advancements.

Keywords: precision medicine,artificial intelligence,foundation model,cancer treatment,oncology,biomarkers,drug discovery,cdx
Mindpax platform: Optimization and development of a novel wearable screening tool for Severe Mental Disorders. (Mindpax platform)

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

Mental disorders, including bipolar disorder and schizophrenia, affect over 60 million people globally, representing the most prevalent severe mental illnesses (SMIs). These conditions often overlap and co-occur, complicating diagnosis and treatment. Challenges include incoherent monitoring practices, lack of comprehensive data-driven analytics, high treatment costs, and a significantly reduced quality of life for patients. Current monitoring methods are subjective and limited, leading to delays in diagnosis and inappropriate treatments. Mindpax addresses these gaps with a novel solution: a comprehensive hardware and software platform for continuous monitoring of sleep patterns and clinical states. This includes a wearable wristband and an AI-integrated mobile application that tracks actigraphy data, offering real-time insights into patients' conditions. The platform leverages a proprietary algorithm, validated on a dataset spanning 523 years, to accurately detect sleep patterns and mood states, providing personalized digital therapeutics. This data-driven approach enables early detection of relapses and tailored interventions, significantly improving symptom management and reducing healthcare costs. The European Innovation Council (EIC) support is critical for Mindpax to advance its development. The funding will be used for two key activities: developing a Novel Phenotyping Module to integrate advanced precision medicine using Polygenic Risk Scores (PRS) and market entry for bipolar and schizophrenia solutions in Germany. This includes regulatory navigation, clinical validation, and strategic partnerships. EIC support will bridge the funding gap, enabling clinical validation, enhancing credibility, and facilitating commercialization. With this backing, Mindpax aims to revolutionize mental health care through innovative digital solutions, ultimately improving patient outcomes and reducing economic burdens.

Keywords: wellbeing patient care personalised treatment neurodegenerative disorders healthcare system bipolar disorder schizophrenia severe mental illness
Atomic Scale Defects: Structure and Function (AtoFun)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Synthetic Chemistry and Materials
Panel description: New materials and new synthetic approaches, structure-properties relations, solid state chemistry, molecular architecture, organic chemistry
Countries involved: United Kingdom
Call: ERC Starting Grant (ERC-2016-STG, 2016)
Start date: 2017-03-01
End date: 2023-04-30
Duration (months): 74.0
Total project cost (EUR): 1610231.0
EU funding (EUR): 1610231.0
Participants: 1
Project status: SIGNED

Atomic scale defects play a key role in determining the behaviour of all crystalline materials, profoundly modifying mechanical, thermal and electrical properties. Many current technological applications make do with phenomenological descriptions of these effects; yet myriad intriguing questions about the fundamental link between defect structure and material function remain. Transmission electron microscopy revolutionised the study of atomic scale defects by enabling their direct imaging. The novel coherent X-ray diffraction techniques developed in this project promise a similar advancement, making it possible to probe the strain fields that govern defect interactions in 3D with high spatial resolution (<10 nm). They will allow us to clarify the effect of impurities and retained gas on dislocation strain fields, shedding light on opportunities to engineer dislocation properties. The exceptional strain sensitivity of coherent diffraction will enable us to explore the fundamental mechanisms governing the behaviour of ion-implantation-induced point defects that are invisible to TEM. While we concentrate on dislocations and point defects, the new techniques will apply to all crystalline materials where defects are important. Our characterisation of defect structure will be combined with laser transient grating measurements of thermal transport changes due to specific defect populations. This unique multifaceted perspective of defect behaviour will transform our ability to devise modelling approaches linking defect structure to material function. Our proof-of-concept results highlight the feasibility of this ambitious research project. It opens up a vast range of exciting possibilities to gain a deep, fundamental understanding of atomic scale defects and their effect on material function. This is an essential prerequisite for exploiting and engineering defects to enhance material properties.

Keywords: coherent x ray diffraction,dislocations,atomic scale point defects,ion implantation,thermal transport,lattice strain
Photonics Cellulose Pigments (PixCell)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Countries involved: United Kingdom
Call: ERC-Proof of Concept (ERC-2017-PoC, 2017)
Start date: 2018-10-01
End date: 2020-03-31
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: CLOSED

The objective of the proposal is to develop a process for large-scale fabrication of a novel class of cellulose-based photonic pigments, which will replace traditional, potentially hazardous colorants that are currently used in industry for cosmetics, food, textiles and security labelling. These novel photonic pigments consist of micron-sized spherical particles composed of naturally-derived cellulose nanocrystals. Within the remit of the ERC StG SeSaMe, it has been demonstrated that cellulose nanocrystals can self-assemble in aqueous droplets, forming dry microparticles, where the crystals are organised in chiral nematic ordered shells, capable of reflecting light of a specific colour. Scaling up the fabrication of such photonic cellulose pigments will allow to manufacture a truly sustainable, biocompatible and potentially edible alternative to conventional synthetic dyes for mass-market applications.

Keywords: photonics pigment,sustainable materials