Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
REVisiting Antibody structures and repertoires through advances in Mass spectrometry and Proteomics (REVAMP)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced 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: Netherlands
Call: ERC ADVANCED GRANTS (ERC-2023-ADG, 2023)
Start date: 2025-01-01
End date: 2029-12-31
Duration (months): 60.0
Total project cost (EUR): 2500000.0
EU funding (EUR): 2500000.0
Participants: 1
Project status: SIGNED

In REVAMP, I will create innovative structural mass spectrometry (MS) and proteomics techniques to generate an unprecedented molecular understanding of the diversity and structural and functional characteristics of human antibody repertoires. These repertoires are highly personal and play a key role in our human immune response. Recently, my research has taken a novel challenging avenue, with a focus on the development of cutting-edge MS-based techniques to better understand antibody structure and function. With these methods, I have started to monitor at the protein level (changes in) endogenous antibody repertoires with clonal resolution, including those induced by antigen-specific polyclonal responses in individuals. Early studies were limited to IgG1, and in REVAMP, I aim to expand the toolbox to the full antibody repertoire, including all (sub)classes of antibodies. I aim to learn how and why antibody profiles in serum, and other body fluids, are alike or distinct, investigating structural, clonal and functional differences over time and between individuals. I will develop technologies to target the sub-repertoires recognizing antigens from pathogens. Following identification by advanced top-down and bottom up de novo sequencing these antibodies will be followed up functionally as potential therapeutic leads. Special attention will be given to IgM, the largest human antibody, appearing first in the response to an antigen. We recently made the preliminary striking observation that its structure is ill-defined in textbooks, and that human IgM may be quite different from IgM in other mammals. This project will benefit from our recent advances in structural analysis by single molecule mass spectrometry, mass photometry, atomic force microscopy and electron microscopy. REVAMP will overhaul our current view of the structural diversity of our human antibody repertoire and thereby elevate our understanding of how the human immune system functions.

Keywords: immunoglobulins mass spectrometry secreted antibodies
Quantitative analysis of electrocatalytic reactions using transient voltammetry (QUAER-TV)

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: Chemistry
Countries involved: Netherlands
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2026-08-01
End date: 2028-07-31
Duration (months): 24.0
Total project cost (EUR): 232916.16
EU funding (EUR): 232916.16
Participants: 1
Project status: SIGNED

This proposal aims to develop a rigorous quantitative framework for analyzing electrocatalytic reactions, addressing a critical gap between experimental studies and theoretical models in the field of electrocatalysis. The project will combine Fourier-transformed ac voltammetry (FTACV) measurements on single-crystal electrodes (SCEs) with Bayesian inference techniques to validate and test kinetic models describing electrocatalytic reactions. The objectives are to: (1) Validate kinetic models for simple electron transfer reactions on well-defined surfaces, (2) Quantify the effects of reaction environment modifications on validated kinetic models, and (3) Extract with high-confidence reaction parameters for important electrocatalytic reactions like hydrogen evolution/oxidation and CO2 reduction The innovative methodology integrates the high kinetic sensitivity FTACV, the well-defined nature of SCEs, and the statistical power of Bayesian inference. This combination will enable rigorous model validation, parameter estimation with confidence intervals, and quantitative comparison between competing models. By establishing clear validity boundaries for kinetic models and relating physical processes to model parameters, this work will strengthen the foundation for accurate model-experiment comparisons in electrocatalysis. The outcomes will be significant for developing more active, durable, and selective catalysts, as well as advancing theoretical modeling of electrocatalytic reactions. The project will contribute to the development of electrochemical technologies crucial for reducing the environmental impact of energy and chemical production.

Keywords: single crystal metal electrodes,electrocatalysis,fourier transformed ac voltammetry,microkinetic modeling
Robust attribution of human-induced thermodynamic and dynamic contributions in historical changes of regional heat and cold waves over Europe (EXTREME)

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: Sweden
Call: MSCA Postdoctoral Fellowships 2021 (HORIZON-MSCA-2021-PF-01, 2021)
Start date: 2022-06-01
End date: 2024-05-31
Duration (months): 24.0
Total project cost (EUR): 206887.68
EU funding (EUR): 206887.68
Participants: 1
Project status: CLOSED

The exceptionally increasing European heat and cold waves in recent decades have been widely attributed to global warming from a thermodynamic perspective. However, contribution of warming-induced circulation changes to such regional waves (termed the 'human-induced dynamic contribution') remains largely unknown, while midlatitude atmospheric circulations have been altering under global warming. Human fingerprint will not be fully detected if the human-induced dynamic contribution is not included. Dr. Chunlüe Zhou who has an outstanding academic profile in daily data homogenization and regional climate change attribution, will interact with Prof. Deliang Chen (University of Gothenburg, Sweden) to make a robust attribution to quantify human-induced thermodynamic and dynamic contributions in historical changes of regional heat and cold waves over Europe. Human activities include greenhouse gas emissions, aerosol emissions and land use/cover changes. To make robust the attribution, attribution uncertainties arising from observation, model and attribution method uncertainties will be improved through: i) reducing observation errors by removing spurious shifts in the mean and variance of daily series; ii) improving regional details of heat and cold waves and circulation patterns by downscaling large ensembles of global-simulated data using the Weather Research and Forecasting (WRF) model and exploiting feature-based machine learning recognition; and iii) developing a Bayesian statistical approach to separate the human-induced thermodynamic and dynamic contributions. This robust attribution will address these current challenges, but also push event attribution research to a regional scale, greatly increasing confidence in attribution to human causes. Through this EXTREME project, I will increase my research skills, but also gain adequate soft skills in project management, supervision and dissemination with the ultimate intent to build a vibrant research group in Europe.

Keywords: heat and cold wave temperature extreme extreme event attribution regional climate change and variability global warming land use cover change weather research and forecasting model,wrf cmip6
Not Knowing in Deep Representation Learning (NoKnow)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Computer Science and Informatics
Panel description: Informatics and information systems, computer science, scientific computing, intelligent systems
Countries involved: Denmark
Call: ERC CONSOLIDATOR GRANTS (ERC-2023-COG, 2023)
Start date: 2024-08-01
End date: 2029-07-31
Duration (months): 60.0
Total project cost (EUR): 1999114.0
EU funding (EUR): 1999114.0
Participants: 1
Project status: SIGNED

Machine learning and artificial intelligence techniques are progressing at a tremendous pace and impressive results appear across scientific fields. However, as machine learning models grow in capacity, they become increasingly ‘black box’, and it becomes harder for humans to reason about the patterns discovered by the machine. A root cause of this difficulty is that most machine learning models can express the same pattern in infinitely many, equally good, ways within their internal representations of the world. This is known as an identifiability problem. Today we lack a general solution to identifiability problems, and either give up on understanding the patterns discovered by the machine or reduce model complexity to lessen the problem. The latter also reduces the fidelity and applicability of the model. NoKnow rephrase the question of identifiability to be concerned with tasks solved by the representation rather than the representation itself. Using tools from differential geometry and Bayesian inference, we develop the theoretical tools to ensure that tasks solved in the representation have an identifiable outcome even if the representations themselves are not identifiable. To turn theory into practice, we develop state-of-the-art algorithms for assessing the uncertainty of learned representations in order to indirectly estimate the topology of the representation manifold. We further develop novel, high-fidelity predictive models that have identifiable outcomes when trained on learned representations. NoKnow provides the fundamental tools needed to engage with learned representations to guarantee identifiable outcomes. This, in turn, increase trust in findings as they are not dependent on arbitrariness in learned representations. As society increasingly automates decisions, this trust in machine learning becomes ever more important.

Keywords: identifiability,unsupervised representation learning,unsupervised learning,bayesian inference,approximate bayesian inference,geometric statistics
Co-produced transformative knowledge to accelerate change for biodiversity (RAINFOREST)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Food, Bioeconomy Natural Resources, Agriculture and Environment
Theme: Bio-Environment
Destination: Biodiversity and Ecosystem Services
Action type: Research and Innovation actions
Countries involved: Norway,Austria,Germany,Netherlands,Peru,Switzerland,Cyprus
Call: Assessing the nexus of extraction, production, consumption, trade and behaviour patterns and of climate change action on biodiversity in the context of transformative change (HORIZON-CL6-2022-BIODIV-01, 2022)
Start date: 2022-12-01
End date: 2026-02-28
Duration (months): 39.0
Total project cost (EUR): 3314529.0
EU funding (EUR): 3314529.0
Participants: 11
Project status: SIGNED

Food and biomass production systems are among the most prominent drivers of biodiversity loss worldwide. Halting and reversing the loss of biodiversity therefore requires transformative change of food and biomass systems, addressing the nexus of agricultural production, processing and transport, retailing, consumer preferences and diets, as well as investment, climate action and ecosystem conservation and restoration. The RAINFOREST project will contribute to enabling, upscaling and accelerating transformative change to reduce biodiversity impacts of major food and biomass value chains. Together with stakeholders, we will co-develop and evaluate just and viable transformative change pathways and interventions. We will identify stakeholder preferences for a range of policy and technology-based solutions, as well as governance enablers, for more sustainable food and biomass value chains. We will then evaluate these pathways and solutions using a novel combination of integrated assessment modeling, input-output modeling and life cycle assessment, based on case studies in various stages of the nexus, at different spatial scales and organizational levels. This co-production approach enables the identification and evaluation of just and viable transformative change leverage points, levers and their impacts for conserving biodiversity (SDGs 12, 14-15) that minimize trade-offs with targets related to climate (SDG13) and socio-economic developments (SDGs 1-3). We will elucidate leverage points, impacts, and obstacles for transformative change and provide concrete and actionable recommendations for transformative change for consumers, producers, investors, and policymakers.

Keywords: integrated assessment model,life cycle assessment,input output analysis,transformative change,biodiversity impacts,co production,stakeholder involvement,levers and leverage points,case studies
Terminal Airspace Digital Assistant (TADA)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Public-private partnerships
Destination: Single European Sky Air Traffic Management Research
Action type: Research and Innovation actions
Partnership: SESAR (JU-JTI)
Countries involved: Spain,Italy,Germany,Finland,Malta
Call: ATM application-oriented research for connected and automated ATM (HORIZON-SESAR-2023-DES-ER-02, 2023)
Start date: 2024-09-01
End date: 2027-02-28
Duration (months): 30.0
Total project cost (EUR): 1769978.75
EU funding (EUR): 1769978.75
Participants: 6
Project status: SIGNED

TADA is a project aimed at improving Terminal Airspace (TMA) performance through the use of ATCO generated historical data and ML to provide the ATCO with decision and action selection for future situations, presented in a human centric way. TMAs, especially those serving major airport hubs and/or multi-airport systems, are areas of heavy congested traffic. Busy TMAs could benefit from further automation that would improve capacity, flow and trajectory efficiency and safety. The current ATC paradigm in TMAs consists of having flights and their intentions identified by the air traffic controllers (ATCOs), supported by a series of information acquisition and analysis tools, such as AMAN (providing a sequence), trajectory predictions, safety nets and instruction adherence monitoring, most of which are integrated into the ATM system in use. ATCOs assimilate the information available, incorporate other background information, make decisions and instruct the flights. They also interact with the ATM system to keep it up to date with the decisions and the feedback received from the flights. This ATCO data gathered through this interaction is currently barely used beyond the immediate information update cycles and possibly post ops investigations. This wealth of big-data,together with the introduction of machine learning (ML) algorithms that will learn to predict patterns and ATC instructions can be taken advantage of much more to improve capacity, efficiency and safety by providing decision making support to ATCOs and delegation of certain tasks. A digital assistant and corresponding HMI will be developed through TADA and AMAN will benefit from an improvement through the use of the same data and ML. TADA will be carried out by a consortium of 6 partners from 6 different EU countries including academia, ANSP, ATM system provider and an expert company in AI, bringing complimentary academic, technical, human factors and operational skills and expertise to the project.

Keywords: air traffic control,support to decision making,digital assistant,machine learning,explainable artificial intelligence,terminal airspace,arrival manager,real time simulation
kelP side stReam valOrisaTion to dEvelop new biobased valUe chainS (PROTEUS)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Food, Bioeconomy Natural Resources, Agriculture and Environment
Theme: Public-private partnerships
Destination: Circular Bio-based Europe
Action type: Innovation actions
Partnership: CBE (JU-JTI)
Countries involved: Norway,Netherlands,France,Sweden,Germany,Finland
Call: Valorisation of aquatic biomass waste and residues (HORIZON-JU-CBE-2023, 2023)
Start date: 2024-05-01
End date: 2028-04-30
Duration (months): 48.0
Total project cost (EUR): 14439806.25
EU funding (EUR): 9605076.75
Participants: 11
Project status: SIGNED

Brown algae, particularly L. hyperborea, represent a largely untapped renewable resource in Europe, present in huge volumes along the coastlines of Norway (approximately 60% of global biomass), Sweden, Denmark, Ireland, Iceland, and France. L. hyperborea contains alginate, cellulose, functional polysaccharides like fucoidan and laminarin, phlorotannin’s (polyphenols), as well as protein, vitamins, and minerals, all ingredients in demand in Europe’s biobased value chains. However, current harvesting techniques, particularly widespread use of formaldehyde, is limiting the huge valorisation potential of this promising renewable feedstock. Once brown kelp is exposed to air, chemical and enzymatic reactions quickly begin to rot the harvest. Currently, harvesters mitigate this by treating brown kelp with formaldehyde during and after harvesting. However, formaldehyde causes undesirable browning of some compounds and is toxic, so must be removed through expensive, energy and water intensive washing prior to further processing. This makes valorisation of all but the most valuable alginate fraction (~15% of L. hyperborea biomass) uneconomic; consequently, about 85% of the residual brown kelp biomass is flushed back into the sea unused. PROTEUS addresses the challenge of maximising valorisation of Europe’s L. hyperborea biomass through the optimisation of ALGINOR’s (coordinator) and HYPOMAR’s formaldehyde-free harvesting and extraction technologies and approaches. This will enable large-scale valorisation of Europe’s considerable L. hyperborea resource and conversion of 100% of the biomass into ingredients that will be tested in validated for use by ESSITY (personal-care), OLMIX (feed), VAESS (food), and BORREGAARD (industrial applications) as drop-in replacements for synthetic materials in sustainable biobased product lines.

Keywords: kelp,residual valorisation,alginate,fucoidan,cellulose,kelp fibres,kelp powder,seaweed extracts,sustainable,renewable feedstock,laminaria hyperborea
Negotiating Social Inequalities in Late Prehistoric Cyprus (InequalCyprus)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: The Study of the Human Past
Panel description: Archaeology and history
Countries involved: Netherlands
Call: ERC ADVANCED GRANTS (ERC-2024-ADG, 2024)
Start date: 2025-11-01
End date: 2030-10-31
Duration (months): 60.0
Total project cost (EUR): 2489397.0
EU funding (EUR): 2489397.0
Participants: 1
Project status: SIGNED

The consolidation of social inequalities, postulated to have occurred during the Bronze Age in West Asia, is of crucial importance in the rise of complex societies, yet remains poorly understood. This research project will apply an innovative mixed methods approach to datasets from late prehistoric Cyprus (4000-1700 BCE) to better investigate this key development in global history. Recent archaeological research into social inequalities has either used quantitative methods to demonstrate their emergence (Kohler et al. 2018; Basri and Lawrence 2020) or questioned their existence in early complex societies (Graeber and Wengrow 2021), leaving the question of how social inequalities were first consolidated open. To move this topic forward we need an approach that combines quantitative and qualitative analysis, to assess the significance and durability of social inequalities in prehistoric societies (Klinkenberg and Düring 2023). Late prehistoric Cyprus provides an exceptional possibility to research this topic. Decades of research have produced rich datasets from settlements and cemeteries, that suggest clear transformations in the articulation of social inequalities. In the Chalcolithic (4000-2500 BCE) we have evidence for short-lived social inequalities, especially in house sizes. By contrast, in the Prehistoric Bronze Age (2500-1700 BCE), social inequalities appear more consolidated, for example in graves. This project will investigate social inequalities in everyday life (houses, objects, mobility, and diet), and in performed settings (burials, feasts, and figurative objects), to reconstruct how social inequalities featured in these social arenas. The aim is to both quantify evidence for social inequalities, and study how inequalities were culturally negotiated. The results will shed light on how and why social inequalities were consolidated in late prehistoric societies in Cyprus and contribute to broader discussions on the emergence of social inequalities

Keywords: social inequality migration exchange networks figurative objects house biographies burial practices food feasting