Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
A binary sensor with single-molecule digit to discriminate biofluids enclosing zero or at least one biomarker (NoOne)

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: Finland,Italy
Call: ERC STARTING GRANTS (ERC-2021-STG, 2021)
Start date: 2022-04-01
End date: 2027-03-31
Duration (months): 60.0
Total project cost (EUR): 2499579.0
EU funding (EUR): 2499579.0
Participants: 2
Project status: SIGNED

Biomarkers are measurable indicators of a particular disease state of an organism. There has been an increasing demand for diagnostic markers, enabling reliable and non-invasive screening of peripheral biofluids. It is also established that a universal screening against life-threatening diseases, such as cancer, or infectious disease outbreaks, can be accomplished only by combining genomic and protein marker-based tests. The NoOne project aims at conceiving, engineering, fabricating and validating a ground-breaking platform based on a single-molecule binary bioelectronic sensor, capable to reliably discriminate biofluid samples enclosing zero biomarkers from those containing just one. The technology can be used for ultimate binary sensing of both proteins/peptides and genomic markers to enable the reliable screening of diseases such as cancer as well as viral and bacterial infections. The NoOne binary platform is designed to be portable, cost-effective, easy to operate and with a time-to-results within one hour; hence it is the ideal candidate for point-of-care applications. The prototype will enable clinicians, phytopathologists or veterinaries, to identify the set of samples that are totally free from a protein, peptide or genomic marker as well as from a pathogen (virus or bacteria), from those enclosing at least one with a confidence level of 99%. This makes the NoOne platform the best performing ever in enabling a fast, highly reliable, cost-effective identification of the subset of biological samples belonging to the potentially diseased part of a population. This is of paramount importance for predictive screening of humans, plants or animals. NoOne will demonstrate its effectiveness in key relevant applications such as the binary detection of pancreatic cancer biomarkers, SARS-CoV-2 virus, the Xylella Fastidiosa bacterium and the assay of post translational peptides evidencing the phosphorylated forms regulating crosstalk with oncogenic signalling pathways.

Keywords: biosensors,organic bioelectronics,analytical chemistry,biological markers screening,organic filed effect transistors
Understanding degradation mechanisms of metallic materials in extreme environments using element-resolved electrochemistry (URANUS)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Physical and Analytical Chemical Sciences
Panel description: Analytical chemistry, chemical theory, physical chemistry/chemical physics
Countries involved: France
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): 1499760.0
EU funding (EUR): 1499760.0
Participants: 2
Project status: SIGNED

Evaluating and fabricating sustainable materials for the energy sector is crucial in addressing the current energy crisis and climate change. Despite safety and sustainability concerns, especially as the early generations of nuclear power plants (NPP) near the end of their operational lifespans, NPPs produce almost one-fifth of the world’s electrical energy. However, controlling material degradation and reprocessing issues in NPPs remains surprisingly understudied due to the extreme operating conditions. Up to 95% of uranium from spent nuclear fuel can be recycled through reprocessing in concentrated nitric acid at temperatures exceeding 100°C, although the underlying mechanisms and the role of alloying elements in the fuel container are not yet well understood. Electrochemical methods have been applied in these extreme electrolytes to investigate materials degradation mechanisms providing in situ global responses. However, these methods are limited as they rely on assumptions developed in aqueous media and do not provide direct, element-specific information, necessitating complementary ex situ surface characterizations. To bridge the gap, the URANUS project will develop a novel element-resolved electrochemistry setup for extreme electrolytes, leveraging the unique expertise of the PI in this field. Element-by-element degradation mechanisms of metallic materials in concentrated nitric acid will be elucidated for the first time, tracking the fate of each alloying element. Other non-aqueous electrolytes including molten salt systems will also be investigated with this approach. This will open new avenues for controlling the corrosion of materials in the energy sector. The unique element-resolved database generated from this project will serve as input parameters for training machine learning models aimed at efficiently discovering optimal alloys in specific environments, thereby reducing the consumption of raw and scarce materials during the materials design process.

Keywords: in situ element resolved electrochemistry aesec corrosion mechanism in extreme electrolytes spent nuclear waste reprocessing stainless steels ni alloys database for alloy design
NANO-PERSONALITY: ENGINEERING AND MANIPULATING GREEN SOLVENTS BY NANO-BUBBLES (NIMBLE) (NIMBLE)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced 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 ADVANCED GRANTS (ERC-2022-ADG, 2022)
Start date: 2024-09-01
End date: 2029-08-31
Duration (months): 60.0
Total project cost (EUR): 2461515.0
EU funding (EUR): 2461515.0
Participants: 1
Project status: SIGNED

Nano-bubbles exhibit several unique physical and mechanical characteristics, such as dramatically reduced buoyancy, extremely high surface area/volume ratio, large zeta potentials, enhanced solubility of gas in water. These properties render them good candidates for several commercial applications, such as fine-particle flotation, wastewater treatment, and in food and agricultural industries. A most important challenge lies in establishing facile and easily-controlled methods to promote nano-bubble formation, and, indeed, liquid-phase nano-droplets, i.e., in realising reproducibly and consistently a nano-phase. NIMBLE revolutionises formation of the nano-phase, providing substantial enhancement in effective gas/liquid solubility in water and aqueous media. Further, energy demands are very low visà-vis other nanobubble-generating technologies, with nanobubble stability over months. A ‘Grand Challenge’ lies in understanding underlying mechanistic phenomena involved in nano-phase formation, and the metastability of pure nanobubbles. Indeed, developing experimental and theoretical insights into controlled, on-demand release for nanobubbles is also vital for efficient process-engineering applications. In this ERC ‘NIMBLE’ project, state-of-the-art computer-simulation methods in molecular and larger- (continuum-) scale will be employed in tandem with advanced experimental set-ups and techniques to investigate and manipulate mechanisms of nano-phase formation in the presence of electric fields (Work-Package 1), as well as its controlled, on-demand release (Work-Package 4), with applications to carbon capture and agriculture using nanobubbles’ “carrier” personality. NIMBLE will employ state-of-the-art experimental and simulation methods to investigate and manipulate nano-phase formation in electric fields and controlled release and study their mobility and carrier agency, with applications in carbon capture, water treatment and agriculture.

Keywords: nano bubbles
X-chromosome driven speciation through testes-expressed genes: comparative population genomics meets scRNA analysis in primates (Xspect)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Environmental B iology, Ecology  and E volution
Panel description: For all organisms : Ecology, biodiversity, environmental change , evolutionary biology, behavioural ecology, microbial ecology , marine biology, ecophysiology, theoretical developments and mod elling
Countries involved: Denmark
Call: ERC ADVANCED GRANTS (ERC-2021-ADG, 2021)
Start date: 2022-10-01
End date: 2027-09-30
Duration (months): 60.0
Total project cost (EUR): 2499369.0
EU funding (EUR): 2499369.0
Participants: 1
Project status: SIGNED

Primate X chromosomes evolve extraordinary fast and are also tightly associated with the establishment of reproductive barriers between emerging species. The hypothesis of this proposal is that genetic conflicts between the X chromosome and the rest of the genome during spermatogenesis cause rapid X chromosome evolution and build reproductive barriers. Genetic conflicts for transmission to haploid gametes, called meiotic drive, will cause non-adaptive evolution, which is expected to be countered by other genomic elements that will then be under selection. Such an arms race is expected to lead to a very rapid evolution of the X chromosome and a fast accumulation of incompatibilities between isolated populations, leading to speciation. The goal of the project is to identify the underlying mechanisms and the genes responsible for meiotic drive using primates as the study system. A priori candidate processes include X-linked genes under repeated fast evolution with a focus on genes targeted by pachytene piRNAs and on ampliconic genes. Population genomics analysis will generate specific hypotheses that will then be tested by following expression of candidate genes during spermatogenesis through scRNAseq, and validate findings by ultrasensitive, in situ, staining of single transcripts and immunohistochemistry. Finally, the behaviour of key genes and processes will then be investigated in incipient speciation events. Specifically, 850 individuals of 250 species of primates with full genome data will be analysed for candidate genes on the X chromosome. These genes will be investigated in large scale comparative scRNA sequencing analyses of >10,000 individual testicular cells from 14 primate species, including all great ape species, thus allowing expression trajectories through spermatogenesis to be inferred and followed up in functional experiments. The success criterion is to report on primate speciation genes together with their biological mode of action.

Keywords: primates,spermatogenesis,meiotic drive,speciation,natural selection,scrna sequencing,genetic conflict,incomplete lineage sorting,haploid selection
Space Debris Mapping with Photonic Integrated Circuit Lidar (SPIDAR)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Pathfinder
Destination: Challenges
Action type: EIC actions
Countries involved: Finland
Call: Strengthening the sustainability and resilience of EU space infrastructure (HORIZON-EIC-2024-PATHFINDERCHALLENGES-01, 2024)
Start date: 2025-09-01
End date: 2029-08-31
Duration (months): 48.0
Total project cost (EUR): 1120174.98
EU funding (EUR): 1120174.98
Participants: 1
Project status: SIGNED

The SPIDAR project addresses the growing threat of space debris by developing a low-cost, easily deployable system for in-situ detection of small space debris (SSD) using photonic integrated circuit (PIC)-based lidar technology. SSD, particularly debris smaller than 10 cm in size is challenging to detect with traditional methods such as ground-based radar but still poses significant risks to operational satellites. SPIDAR will leverage the advantages of photonic integration to create an efficient, compact CubeSat-compatible lidar system capable of detecting debris as small as 1 mm in a virtual detection area of 1 m². This project involves development of a novel gallium arsenide (GaAs) based PIC platform with laser output power >500 mW and a wavelength near 1 µm. This system will drastically reduce size, weight, and power consumption compared to similar lidars at this wavelength, making it suitable for small satellites like CubeSats. The project is divided into three key goals: the design and fabrication of the GaAs-based lidar PIC; demonstration of lidar functionality in a laboratory setting; and the development and validation of a satellite payload demonstrator. The lidar system will use a frequency modulated continuous wave (FMCW) architecture, with all essential transmitter components integrated on a single chip. SPIDAR will demonstrate its ability to detect SSD in a lab environment and validate the system's performance through environmental testing to ensure its suitability for space deployment. By developing a CubeSat-compatible, PIC-based lidar system, the SPIDAR project contributes to the protection of space infrastructure by enabling more accurate mapping and detection of SSD in low Earth orbit. The scalable, low-cost nature of this technology makes it a critical tool for future space debris monitoring missions, enhancing space situational awareness and mitigating collision risks.

Keywords: photonic integrated circuits,gaas,laser,semiconductor laser,lidar,space debris,satellites,cubesat
Transforming Road Safety in Africa (TRANS-SAFE)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Mobility
Destination: Safe, Resilient Transport and Smart Mobility services for passengers and goods
Action type: Research and Innovation actions
Countries involved: Germany,Ghana,Sweden,Czechia,Italy,Kenya,Côte d'Ivoire,Belgium,Rwanda,South Africa,Nigeria,Zambia,Morocco,United Kingdom,Switzerland,Spain
Call: Radical improvement of road safety in low and medium income countries in Africa (HORIZON-CL5-2021-D6-01, 2021)
Start date: 2022-09-01
End date: 2026-08-31
Duration (months): 48.0
Total project cost (EUR): 3883375.0
EU funding (EUR): 3800875.0
Participants: 28
Project status: SIGNED

The TRANS-SAFE project will involve national, regional, and city level demonstrations to test different types of innovative and integrated Safe System solutions, complemented by a comprehensive toolbox, capacity development, policy support and replication activities. To maximize impact, the project brings together in a consortium, highly committed cities, road safety agencies and experts from both Europe and Africa. Building on numerous synergistic projects, networks, and a strong technical experience among partners, the consortium will deliver an ambitious project through highly effective and innovative approaches to sustainable road safety development, thereby ensuring that road safety systems and interventions from this project deliver on the recommendations of the Road Safety Cluster of the African-EU Transport Task Force, adopted in 2020. The consortium members have experience and expertise in Africa-related research as well as development-related research in collaboration with local actors in various countries of Africa at many levels. Ultimately, the project will help deliver on the Joint EU-Africa Strategy (JAES) and advance countries' progress towards the 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDGs).

Keywords: safe system
Incrementally learning new classes with generative classification (GenClassifier4CL)

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: Belgium
Call: MSCA Postdoctoral Fellowships 2021 (HORIZON-MSCA-2021-PF-01, 2021)
Start date: 2022-10-01
End date: 2024-09-30
Duration (months): 24.0
Total project cost (EUR): 191760.0
EU funding (EUR): 191760.0
Participants: 1
Project status: CLOSED

Learning continually from non-stationary streams of data is a key feature of natural intelligence, but an unsolved problem in deep learning. Particularly challenging for deep neural networks is the problem of "class-incremental learning", whereby a network must learn to distinguish classes that are not observed together. In deep learning, the default approach to classification is learning discriminative classifiers. This works great in the i.i.d. setting when all classes are available simultaneously, but when new classes must be learned incrementally, successful training of discriminative classifiers depends on workarounds such as storing data or generative replay. In a radical shift of gears, here I propose to instead address class-incremental learning with generative classification. Key advantage is that generative classifiers – unlike discriminative classifiers – do not compare classes during training, but only during inference (i.e., when making a classification decision). As a proof-of-concept, in preliminary work I showed that a naïve implementation of a generative classifier, with a separate variational autoencoder model per class and likelihood estimation through importance sampling, outperforms comparable generative replay methods. To improve the efficiency, scalability, and performance of this generative classifier, I propose four further modifications: (1) move the generative modelling objective from the raw inputs to an intermediate network layer; (2) share the encoder network between classes, but not necessarily the decoder networks; (3) use fewer importance samples for unlikely classes; and (4) make classification decisions hierarchical. This way, during my MSCA fellowship hosted in the group of Prof Tinne Tuytelaars, I hope to develop generative classification into a practical, efficient, and scalable state-of-the-art deep learning method for class-incremental learning.

Keywords: continual learning deep neural networks catastrophic forgetting generative classification class incremental learning
Ocean incursion and melting near ice-sheet grounding lines via high-resolution large-eddy simulations (GLmelt)

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: France,United Kingdom
Call: MSCA Postdoctoral Fellowships 2022 (HORIZON-MSCA-2022-PF-01, 2022)
Start date: 2024-04-01
End date: 2026-03-31
Duration (months): 24.0
Total project cost (EUR): 211754.88
EU funding (EUR): 211754.88
Participants: 2
Project status: CLOSED

Ice shelf melting and retreat are accelerating in polar regions because the warm off-shore waters are gaining greater access to the base of ice shelves. Warm waters have the potential of reaching the grounding line and thereby melting it causing the retreat of the full ice shelf, resulting in elevated rates of ice discharge into polar seas and sea level rise. The interconnection between ocean conditions, warming, and ice melting in ice-shelf cavities is poorly understood due to the paucity of observations and turbulence-resolving simulations. This knowledge gap hinders future projections of ice changes and makes it impossible to predict if and when tipping points will be crossed within the 21st century, as their timing critically depends on the details of ice-ocean interactions. GLmelt aims to fill the critical knowledge gap pertaining to the relationship between ice melting rate and ocean conditions by running numerical simulations capable of resolving turbulence. They focus on near-grounding-line regions where ice sheet sensitivity to ice melting is greatest. GLmelt objectives are: 1. Developing an open-source numerical model capable of resolving under-ice ocean dynamics near grounding lines 2. Investigating melt rate distributions considering a range of ice-shelf cavity configurations found in nature 3. Investigating how the characteristic melt rate distributions vary with problem parameters in quiescent ocean conditions 4. Quantifying the effect of long-shore currents 5. Organizing a workshop reviewing state-of-the-art knowledge in dynamical processes controlling basal ice melting and guiding practices in choosing melting parameterizations GLmelt will enable the candidate fellow to deliver high-impact research outcomes to the research community, environmental policymakers, and the public while receiving high-quality training from world-leading institutes in hydrodynamic turbulence and polar sciences, which will help him secure a permanent research position.

Keywords: ocean dynamics,ice shelf grounding line,ice shelf cavity,ice ocean interaction,basal melting parameterization,ocean turbulence,sea level rise,climate change,large eddy simulation