Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Deep learning for Forest Mapping using Point clouds (DeepForMaP)

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: France
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2026-06-01
End date: 2028-05-31
Duration (months): 24.0
Total project cost (EUR): 226420.56
EU funding (EUR): 226420.56
Participants: 1
Project status: SIGNED

Over the last decade, the Earth has witnessed significant climate change and global warming, largely as consequences of rapid industrial development. To combat this, the management and preservation of forests have become increasingly essential, given their homeostatic role in regulating the global climate. Point clouds (PCs) obtained from LiDAR mapping offer vast amounts of data, which have proven invaluable for forest mapping. In our research, DeepForMaP, we leverage PC data and utilise advanced deep learning techniques to monitor forest patterns across both space and time. One of the primary challenges in processing PC data arises from its unstructured nature. To address this, we employ recent feature extractors such as Mamba and Kolmogorov Arnold Networks (KANs) to process the data, extracting meaningful and discriminative representations that result in more accurate classification and detection. Furthermore, given the high point density of PCs (often in billions), data annotation is limited. To overcome this, we employ self-supervised learning and zero-shot learning techniques. The former capitalises on the inherent structure of PCs to design specific tasks that train feature extractors, while the latter leverages semantic information from the corresponding PC descriptions to manage data scarcity. Our trained models are applied to classification and change detection tasks, and we also evaluate their performance in cross-domain settings, where training and testing occur in geographically disjointed areas. This project is expected to result in efficient deep learning models, deployable in practical forest mapping scenarios. DeepForMaP will be hosted at IRISA laboratory at Université Bretagne Sud Vannes under the esteemed supervision of Prof. Sébastien Lefèvre and Dr. Minh-Tan Pham. This will offer the candidate invaluable exposure to cutting edge scientific development in earth observation and and equip him to become an independent researcher in the future.

Keywords: deep learning,point clouds,forest mapping
Ice Shelf Damage Characterization and Monitoring around Antarctica (IceDaM)

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): 1478971.0
EU funding (EUR): 1478971.0
Participants: 1
Project status: SIGNED

The ice shelves of Antarctica act as giant dams limiting ice flow from the interior into the ocean. Recently, several of these dams have shown dramatic signs of damage. Once the ice is fracturing, the dam is weakened, allowing the ice to flow faster and increasing mass loss from the interior of the ice sheet, which results in an accelerated contribution to sea-level. As Antarctica is the largest reservoir of fresh water, correctly predicting its evolution is crucial to anticipate when and how much sea-level will rise. Damage is currently absent from projection of Antarctica evolution, because of the difficulty of representing this process in numerical models as well as the lack of observation to constrain them. The aim of the IceDaM project is to quantify and understand the evolution of damage on ice shelves around Antarctica. My team and I will first use a novel approach based on deep learning to automatically identify the evolution of fractures and their characteristics on satellite imagery. By combining this record with inversions from a high-order ice flow model, we will quantify the links between fractures and changes in ice rigidity, which controls the strength of ice shelves. To better understand the damage variability, we will measure the evolution of key variables that positively impact the rheological weakening of ice shelves. These time series will be analyzed in a unique fashion, to determine the major processes that led to the evolution of damage in the satellite observation era. Based on these results, we will set up the first “sentinel” of ice shelves by systematically mapping the evolution of fractures in near real time. This will be used to establish new vulnerability indices, based on changes in ice rigidity and their impact on glacier mass balance. This project will open a new window on the processes affecting ice shelves at an unprecedented level of resolution, ultimately allowing us to improve our ability to predict the fate of sea level rise.

Keywords: ice shelves,ice dynamics,ice ocean interactions,satellite altimetry,photogrammetry,image segmentation
Lineage tracing of the human brain (LINMOS)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Neuroscience and Disorders of the Nervous System
Panel description: Nervous system development, homeostasis and ageing , nervous system  function and dysfunction , systems neuroscience  and modelling , biological basis of cognitive processes and of behaviour, neurological and mental disorders – In humans and all other organisms
Countries involved: France
Call: ERC STARTING GRANTS (ERC-2023-STG, 2023)
Start date: 2024-06-01
End date: 2029-05-31
Duration (months): 60.0
Total project cost (EUR): 1800000.0
EU funding (EUR): 1800000.0
Participants: 3
Project status: SIGNED

Organisms develop through cell divisions generating clones that progressively organize to form different tissues. This process can be visualized as a cell phylogeny by lineage tracing. In order to track cell lineages, each cell division has to be permanently and uniquely marked. In model systems, this is done by genome editing. In humans this is not applicable, which has historically made impossible to accurately study cell phylogenies. Very recently, however, studies showed that human tissues contain a set of natural markers in the form of somatic DNA variants that permanently and cumulatively label every cell division during development. My work showed that somatic variants are reliable at tracking cell lineages directly in human tissues. Human brain development has historically been difficult to address since human-brain-specific features are hardly recapitulated in any existing model. My project now builds on the use of somatic variants to reveal the cellular processes that form the brain starting from available human material. Somatic variants will be identified at unprecedent sensitivity in a large set of human foetal tissues, including multiple different brain regions. In work package (WP)1, I will identify and study the lineage segregation steps defining the brain phylogeny starting from the zygote. Mathematical modelling will elucidate mechanisms of clone dynamics. In WP2, I will focus on the cerebral cortex and reconstruct cell phylogenies of neural progenitors and the distribution of cortical clones. Furthermore, I will compare normal versus overgrowth cortical malformation brains, where cell proliferation and migration are altered. In WP3, I will develop new strategies to simultaneously assess cell phylogenies and cell-type specification by identifying somatic variants in multi-omics data. This project will reveal the unfolding of the cellular lineages building the human brain, while providing a methodology that is applicable to any tissue and pathology.

Keywords: human brain development,lineage tracing,cell divisions,somatic mutations,cortical malformations
CLIMAte risk and vulnerability Assessment framework and toolboX (CLIMAAX)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Mission
Theme: Adaptation to climate change
Destination: Adaptation to climate change
Action type: Research and Innovation actions
Countries involved: Netherlands,Spain,Italy,Finland,Belgium,Germany,Poland,United Kingdom,Slovakia,Portugal,Austria,Latvia
Call: Development of climate change risk assessments in European regions and communities based on a transparent and harmonised Climate Risk Assessment approach (HORIZON-MISS-2021-CLIMA-02, 2021)
Start date: 2023-01-01
End date: 2027-02-28
Duration (months): 50.0
Total project cost (EUR): 20002272.5
EU funding (EUR): 19999772.5
Participants: 19
Project status: SIGNED

Many European regions and communities have limited experience and resources to integrate available local and global data, models, and concepts into a context-specific Climate Risk Assessment (CRA). For an authoritative CRA, local data and evidence need to be integrated with established pan-European reference data and scenarios. CLIMAAX will provide substantial financial, analytical and practical support to regions and communities to develop and improve their CRAs by a number of activities. CLIMAAX will deliver (a) a standardized CRA framework CRA built on current community experience and best-practices, (b) a Toolbox with data, models and utilities to provide access to European and global open data archives integrated with local data and procedures, (c) five European pilot regional CRAs to shape the framework and toolset, (d) financial support for at least 50 regions to execute a context-specific CRA, (e) CRA guidance material and online helpdesk for other European regions, and (f) a proposal to upscale results into the future operationalization of the regional CRA support function. The main target groups are regional authorities, community organizations (sectoral stakeholder organizations, NGOs, citizen networks) and Civil Protection Agencies focusing on climate risk management and emergency response. The project will engage a large Community of Practice to co-creates the framework and toolbox and co-define the financial support application and monitoring criteria. The main outcome will be a demonstrated acceleration of the design and implementation of regional risk management plans for climate adaptation and emergency response.

Keywords: climate,emergency,risk management plans,climate adaptation,community of practice
Personalised Mechanobiological Models to Predict Tumour Growth and Anti-Cancer Drug Penetration (MechanoGAP)

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: Ireland
Call: ERC STARTING GRANTS (ERC-2023-STG, 2023)
Start date: 2024-01-01
End date: 2028-12-31
Duration (months): 60.0
Total project cost (EUR): 1499693.0
EU funding (EUR): 1499693.0
Participants: 1
Project status: SIGNED

Personalised cancer medicine presents an exciting frontier in healthcare that tailors disease mitigation and intervention to an individual patient. However, existing technologies fail to leverage the physical forces that underpin stress-dependent tumour growth and the subsequent evolution of biomechanical resistance to anti-cancer drugs. Furthermore, the fundamental mechanisms governing such force-sensitivity have yet to be uncovered; this deficiency in scientific understanding of the active biomechanical behaviour of tumours and control of drug penetration has hindered the progression of anti-cancer therapy. In this project, an advanced computational modelling framework will first be developed to uncover the mechanisms underlying stress-dependent cell and tissue growth, coupling the thermodynamics of cellular volume control with active force generation and intracellular transport. Novel experimental analysis of 3D tumour spheroid growth and single cell biomechanics will reinforce the framework to gain a new understanding of how mechanical loading can prevent tumour cell division and the role of intracellular exchange in multi-cellular growth control. The models will then be extended to determine the role of growth-induced stress and cell compaction in restricting drug penetration, and whether this can be mitigated by promoting intracellular drug perfusion. Finally, integrated patient-derived computational and tumour organoid models will be developed for prediction of growth and emergent biomechanical resistance to anti-cancer drugs, motivating model-led mechanobiological therapy in an animal model of breast cancer. The overarching objective of this ground-breaking project is to pioneer a personalised healthcare framework for prediction of mechanically-regulated cancer and treatment outcomes, with remarkable potential to drive a paradigm shift in patient-specific diagnosis and treatment of cancer.

Keywords: computational biomechanics cancer mechanobiology tumour growth modelling
CULTIVATE: CO-DESIGNING FOOD SHARING INNOVATION FOR RESILIENCE (CULTIVATE)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Food, Bioeconomy Natural Resources, Agriculture and Environment
Theme: Bio-Environment
Destination: Resilient, inclusive, healthy and green rural, coastal and urban communities
Action type: Innovation actions
Countries involved: Ireland,Netherlands,Spain,Italy,Germany,Sweden,Portugal,Greece,Czechia,United Kingdom
Call: Social innovation in food sharing to strengthen urban communities’ food resilience (HORIZON-CL6-2022-COMMUNITIES-01, 2022)
Start date: 2023-01-01
End date: 2026-12-31
Duration (months): 48.0
Total project cost (EUR): 9079268.75
EU funding (EUR): 9025268.5
Participants: 19
Project status: SIGNED

CULTIVATE uses a multi-actor approach to build sustainability and resilience in urban and peri-urban areas through a ground-breaking online social innovation support platform – The Food Sharing Compass. Built with and for five key stakeholder groups – food sharing initiatives, policy makers, food supply actors, researchers and citizens – the platform will make it possible to navigate diverse food sharing landscapes and cultures, in order to understand, develop, replicate, expand and strengthen sustainable food sharing in Europe, supporting the European Green Deal’s Action Plan, Food 2030, the Farm to Fork Strategy and the EU’s climate change ambitions. The open access Food Sharing Compass will comprise five innovative tools: The SHARECITY200 Database, an interactive automated mapping, tracking and monitoring database of food sharing initiatives that will add more than 100 new locations to the SHARECITY100 Database [https://sharecity.ie/research/sharecity100-database/]; The Food Sharing Calculator, which enables comprehensive and holistic assessment of the costs, benefits and impacts of food sharing for all stakeholders; The Menu of Good Governance, which provides options for developing policy making which facilitates sustainable food sharing; The Library of Citizen Engagement, which supports establishing, expanding and maintaining inclusive participation in sustainable food sharing; The sustainable food sharing Community of Practice, which will be established through a bespoke Amplification Programme to optimise the potential for mutual learning and exploitation of the Food Sharing Compass well beyond the project. In essence, CULTIVATE will establish the EU as the global frontrunner in the development of resilient and inclusive food sharing economies, identifying drivers and implementation gaps and challenging existing theories and practices which currently constrain sustainable food sharing.

Keywords: food sharing social innovation sustainability resilience
Encoded Knowledge: Epistemology of Computer Technology in Modern Economics (ENCODED)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Texts and Concepts
Panel description: Literary studies , literature,  philosophy
Countries involved: Denmark
Call: ERC CONSOLIDATOR GRANTS (ERC-2024-COG, 2024)
Start date: 2025-12-01
End date: 2030-11-30
Duration (months): 60.0
Total project cost (EUR): 1999968.0
EU funding (EUR): 1999968.0
Participants: 1
Project status: SIGNED

Modern economics has become an influential scientific discipline, but its ability to explain, predict, and provide solutions to socioeconomic problems has been questioned. The criticism of and debates about economics, including the philosophical discussions, ignore the role that computer technology has played in economic research. ENCODED argues for putting the focus on digital data and algorithms at the centre of the philosophical analyses of economics to achieve substantial breakthroughs in understanding modern economics and move debates in philosophy of economics beyond the state of the art. The hypothesis of the project is that reliance on computer technology in modern economics has transformative epistemological consequences. To investigate the hypothesis, ENCODED will analyse whether and how computer technology 1) has had an effect on what is being investigated in modern economics, 2) has affected economists’ theorizing and their choices of empirical methods, and whether and how 3) values embedded in this technology have influenced knowledge produced in modern economics. ENCODED will provide an innovative philosophical approach based on insights from philosophy of science, history of economics, science and technology studies, philosophy of data and algorithms. A crucial move to achieve the aim of ENCODED is to focus on the epistemological significance of the aspects of computer technology: digital data and algorithms. The focus on digital data and algorithms allows insights from the various disciplines informing the project to be synthesized. This focus will also allow for the development of innovative analysis of case studies (in decision theory, macroeconomics, financial economics, and empirical research), to address fundamental challenges to the epistemological analysis of economics brought about by computer technology, as well as to examine values encoded in computer technology and their importance for knowledge production in modern economics.

Keywords: values in science values in economics history of economic thought critical studies of data and algorithms philosophy of economics
Energy Activated Citizens and Data-Driven Energy-Secure Communities for a Consumer-Centric Energy System (ENPOWER)

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: Greece,Portugal,Spain,Slovenia,Luxembourg,France,Belgium,Germany,Austria,Ireland,Hungary,Switzerland,Italy
Call: Supporting the action of consumers in the energy market and guide them to act as prosumers, communities and other active forms of active participation in the energy activities (HORIZON-CL5-2022-D3-01, 2022)
Start date: 2023-09-01
End date: 2026-08-31
Duration (months): 36.0
Total project cost (EUR): 7094156.25
EU funding (EUR): 5999718.75
Participants: 30
Project status: SIGNED

ENPOWER will design, develop and demonstrate SSH-driven methodologies, interactive and closed-loop tools, and data-driven services for energy-activated citizens and energy-secure cross-sector communities towards a citizen-centric energy system. We combine leading-edge ICTs with social/behavioural dimensions and with sharing economy and value stacking business models to deploy: 1) a Social Science Framework for energy citizens activation and innovative multi-dimensional incentives for citizens’ participation in energy markets; 2) AI-based consumers clustering and segmentation algorithms; 3) interactive tools and facilitation services for energy community planning; 4) Energy Data Space adaptation to support community-level data-driven activation and interoperable automated privacy and sovereignty-preserving Demand Response (DR); 5) P2P DLT/Blockchain digital marketplace for tokenized energy and non-energy assets reciprocal compensation; 6) data-driven services and apps for energy efficiency and activation performance management; 7) ICT services and Digital Twins for community-level energy optimization and aggregated flexibility management to trade off local self-consumption against grid service provisioning, while boosting a beyond-energy community social welfare; 8) Edge monitoring hubs for automated DR; 9) Business Sandbox for novel sharing economy and social innovation-based business models; 10) methodologies for evaluating different energy community setups, upscaling and replication. ENPOWER framework will be validated along 4 Front Runners energy communities pilots and further replicated in 2 Early Adopters, covering different levels of maturity of communities, to demonstrate increased RES local self-consumption and consumers participation to the energy markets, while nurturing increased local security of supply. ENPOWER Leadership Programme and blueprints will support EU-wide replication and advice regulatory bodies on communities-friendly enabling frameworks.

Keywords: energy citizens ssh consumers activation energy secure communities energy data space marketplace data driven services closed loop tools planning and management demand response business models