Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Deep optimized generation for antimicrobial peptide discovery (DOG-AMP)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Prevention,  Diagnosis and T reatment of Human Diseases
Panel description: Medical technologies  and tools for prevention, diagnosis and treatment of human diseases, therapeutic approaches and interventions , pharmacology, preventative  medicine, epidemiology and public health , digital medicine
Countries involved: Poland,Germany
Call: ERC CONSOLIDATOR GRANTS (ERC-2023-COG, 2023)
Start date: 2024-05-01
End date: 2029-04-30
Duration (months): 60.0
Total project cost (EUR): 1998471.0
EU funding (EUR): 1998471.0
Participants: 2
Project status: SIGNED

The DOG-AMP project will develop cutting-edge methods for Deep Optimized Generation (DOG), and use them to transform the emerging field of AntiMicrobial Peptide (AMP) discovery. Continuous overuse of antibiotics fuels the outgrowth and spread of multi-drug resistant microbial strains. Increasing antimicrobial resistance is already now a major health and economic hazard, and is expected to account for 10 million deaths globally per year by 2050, exceeding deaths caused by cancer. AMPs are short peptides that can actively and selectively kill antibiotic-resistant pathogens, and as such are considered the most promising strategy for fighting antimicrobial resistance. Still, intensive research on AMP did not translate to their success in the clinic, mostly due to their lower activity and safety compared to existing antibiotics. Deep optimized generation has the potential to radically advance AMP discovery, but only once unsolved problems are attacked and open research directions in this area are further explored to reach three major objectives of the DOG-AMP project: i) develop a novel model, geared for deep optimized generation, combining the variational autoencoder framework with probabilistic modeling and algorithms for Pareto (conflicting multi-target) optimization, dealing with data scarcity and bias, generation diversity, and model interpretability; ii) combine the deep optimized generation model into a framework tailored for the specific needs of AMP design, e.g., accounting for AMP clustering, or conflicting features that make AMPs active or toxic; iii) apply the newly developed framework to explore and navigate the space of peptides to select and experimentally validate the best candidates that will supersede existing AMPs and antibiotics in their activity against hazardous microbes and safety. DOG-AMP has the potential to bring breakthroughs in the broad research areas of deep generative modeling, sequence optimization, and AMP discovery.

Keywords: deep generative models,antimicrobial peptide design
Training doctoral researchers in advanced strategies towards energy-neutral wastewater treatment (NEUTEN)

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: Information Science and Engineering
Countries involved: Belgium,Greece,Estonia,Portugal,Netherlands,Denmark,Spain,Sweden,Austria,France
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): 4173792.69
EU funding (EUR): 3949948.8
Participants: 19
Project status: SIGNED

NEUTEN pioneers a comprehensive approach to tackle the two most challenging environmental, health and societal issues including water pollution with key priority pollutants (KPPs) and the need for clean and renewable sources of energy. Current chemical treatment technologies developed for the degradation KPPs are mostly energy-intensive and costly. Established biological wastewater treatment systems, e.g., anaerobic digestion, only remove certain KPPs to a limited extent. NEUTEN will innovate in advanced and sustainable catalytic systems for the generation of energy carriers or the conversion of KPPs from wastewater to energy-dense hydrochar (WP1). Manipulation of the microbial consortia and design of highly novel support structures will also enhance renewable energy carrier generation and superior KPP removal efficiencies from anaerobic digestion systems (WP2). Lastly, the integration of both chemical and biological treatment methods will be investigated, with the ultimate goal of achieving self-sustained wastewater treatment systems (WP3). Implementation of artificial intelligence tools and market behaviour analysis allows for the development of business models that are not only environmentally sound but also economically viable (WP4). The project aims to train 15 highly skilled doctoral candidates, providing them with a unique interdisciplinary and intersectoral research training program by a broad consortium, encompassing 18 intersectoral partners (6 academic, 12 non-academic) in 10 countries. The highly relevant field, combined with an advanced training program that encompasses transversal skills and training through research will ensure that highly trained professionals will enter the job market, further strengthening the competitiveness of the EU in this domain. NEUTEN's holistic approach promises to set a new standard in water treatment technologies, with long-term impacts on environmental protection, public health, and economic sustainability.

Keywords: water pollution renewable energy wastewater treatment key priority pollutants energy self sustained artificial intelligence market behaviour analysis
Inference in High Dimensions: Light-speed Algorithms and Information Limits (INF_2)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Computer Science and Informatics
Panel description: Informatics and information systems, computer science, scientific computing, intelligent systems
Countries involved: Austria
Call: ERC STARTING GRANTS (ERC-2024-STG, 2024)
Start date: 2024-10-01
End date: 2029-09-30
Duration (months): 60.0
Total project cost (EUR): 1662400.0
EU funding (EUR): 1662400.0
Participants: 1
Project status: SIGNED

Extracting information from data is the key challenge of our time, and in many applications (e.g., genome-wide association studies, data compression, and virtual assistants such as ChatGPT) both the data and the machine learning model used to extract information are increasingly high-dimensional. As traditional statistical theory is ill-equipped to face this explosion in the dimensionality of the problem, machine learning is now predominantly experimental. However, empirical approaches come with huge costs affordable only to large companies, and they lack interpretability, which is especially troublesome in medical applications. To address these issues, the INF^2 project develops information-theoretically principled methods for high-dimensional inference in machine learning and data science. The key insight is that, via a “mean-field” approach, high-dimensional quantities are well approximated by low-dimensional ones and then characterized exactly. Leveraging this characterization, we will (i) establish the fundamental limits of inference, i.e., the minimal amount of data necessary to solve the problem, and (ii) design efficient algorithms requiring only the minimal amount of data. The challenge we tackle is to apply this paradigm to practical settings, in which data are structured and heterogeneous (as in genome-wide association studies), and models consist of complex architectures tailored to applications (auto-encoders for data compression, and transformers for ChatGPT). Through a novel analysis of spectral methods, approximate message passing and gradient descent, INF^2 builds a theoretical framework having conceptual impact, as well as vast applicability, in machine learning and information theory. This framework is then brought to the real world via applications in genome-wide association studies. Broadly, our results enable the principled design of machine learning algorithms and models, drastically reducing costs and providing interpretable solutions.

Keywords: theory of machine learning,information theory,high dimensional statistics,genome wide association studies gwas,approximate message passing amp,spectral estimators,neural networks,mean field analysis
DEPENDABLE SMART ENERGY SYSTEMS (DENSE)

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: Information Science and Engineering
Countries involved: Germany,Cyprus,France,United Kingdom,Belgium,Switzerland
Call: MSCA Doctoral Networks 2022 (HORIZON-MSCA-2022-DN-01, 2022)
Start date: 2024-01-01
End date: 2027-12-31
Duration (months): 48.0
Total project cost (EUR): 2707366.46
EU funding (EUR): 2595706.8
Participants: 12
Project status: SIGNED

The DN DENSE is addressing individual research projects and training of Doctoral Candidates (DCs) in the innovative dependable engineering of Smart Energy Systems (SESs) with the main focus on robustness as well as preventive and corrective actions under uncertainty. Within this concept, the term “Smart Energy Systems (SESs)” refers to a holistic cross-sectoral approach (e.g. electricity, heating, cooling, industry, buildings and transportation) aimed at excelling the transformation towards sustainable and achievable future energy systems. Hence, it both covers but also extends beyond the Smart Grid approach, which is mainly focused on the electricity sector. Consequently, SESs exhibit the following attributes: (i) Complex interactions on sub-system and cross-sectoral systems-of-systems levels; (ii) Cyber-physical characteristics with digital and physical network connections; (iii) Ability to operate in non-stationary, uncertain and severe environments. Dependability of complex networks, such as SESs, characterizes their ability to deliver service that can justifiably be trusted. Thus, dependability comprises system attributes, such as availability, reliability, safety, integrity and maintainability. A key requirement of dependability is the desire for providing justifiable trust in the system performance. Hence, rigorous systems engineering yielding provable performance guarantees throughout the system’s life time is already required at the design stage. This challenge is tackled in DENSE with a focus on operational robustness as well as preventive and corrective actions in SESs. As a consequence, DENSE is well-aligned with the EU Commission’s headline ambitions on the European Green Deal as well as the strive for grasping the opportunities from the digital age, while increasing social fairness and prosperity.

Keywords: smart energy artificial intelligence control engineering smart grids energy systems
Sustainable European sodium-ion batteries for stationary applications featuring improved PRocesses, Inherently safe and Non-Toxic materials (SPRINT)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Energy
Destination: Cross-sectoral solutions for the climate transition
Action type: Innovation actions
Partnership: Batteries (co-programmed)
Countries involved: Switzerland,Norway,Netherlands,Italy,Finland,Germany,France,Austria,Lithuania,Spain,Belgium
Call: Non-Li Sustainable Batteries with European Supply Chains for Stationary Storage (Batt4EU Partnership) (HORIZON-CL5-2024-D2-01, 2024)
Start date: 2025-01-01
End date: 2028-10-31
Duration (months): 46.0
Total project cost (EUR): 8173805.52
EU funding (EUR): 6900631.8
Participants: 18
Project status: SIGNED

Sodium-ion batteries offer enhanced cost-effectiveness and sustainability, challenging the dominance of lithium batteries in the stationary energy storage sector. The SPRINT project will gather 8 industries, 2 SMEs and 8 academic partners (incl. one associated partner) for 46 months to optimise and demonstrate two sustainable, techno-economically viable and safe quasi-solid-state sodium-ion batteries better meeting the requirements of stationary energy storage applications. This will be achieved relying on abundant, non-toxic, safe, and competitive materials available in EU supply chains brought to scale within SPRINT, i.e. optimised NFP cathode materials relying on novel synthesis; hard-carbon materials derived from a validated forest residues supply chain in Northern Europe; and quasi-solid-state polymer and polymer composite electrolytes with a solvent-free synthesis, and which are major advancements vs. flammable liquid electrolytes. Strategic interface optimisations will be leveraged to meet end-users’ requirements in terms of cells’ cycle life. SPRINT also aims to bring to scale dry electrode processing (incl. using PFAS-free binders) to enhance the sustainability of battery manufacturing. Batteries encompassing such solutions will be demonstrated on two demonstration sites in Austria (portfolio hybridisation, balancing services) and Lithuania (residential PV, increased grid capacity for EV chargers), and SPRINT will also engage with international use-case providers (e.g. Morrocco, Tunisia, Kenya, Sierra Leone, etc.). All in all, it is foreseen that SPRINT will reduce costs (0.04€/kWh/cycle), enhance energy density (>200Wh/kg & >420Wh/L) and power metrics (>500 W/kg), improve cells' cycle life (projected >5,000 cycles), while ensuring safe operation (leak-free technology) for a high market penetration. The Consortium will accompany all solutions to commercialisation, supported by the created Exploitation Board to facilitate their uptake.

Keywords: sodium ion batteries stationary energy storage quasi solid state dry processing sustainability low cost
Distributional Shifts Underestimate Net Primary Productivity in grazing grassland (UeNPP)

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: Denmark
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2025-10-20
End date: 2027-10-19
Duration (months): 24.0
Total project cost (EUR): 263393.28
EU funding (EUR): 263393.28
Participants: 1
Project status: SIGNED

Accurately estimating aboveground net primary productivity (ANPP), the key component of net primary productivity (NPP) and the primary food source for herbivores, is essential for improving our understanding of the carbon cycle. It is also critical for shaping future policy decisions and advancing sustainable grazing practices, especially in the context of climate change. However, the widespread use of inaccurate ground-truth data in the training and validation of ANPP models has led to problems with data distributional shifts, which reduce the accuracy of these models. In this project, “Distributional shifts Underestimate Net Primary Productivity in grazing grassland” (UeNPP), I will investigate the underlying mechanisms driving the data distributional shift in the simulation of grassland ANPP and provide solutions to improve grassland ANPP modelling accuracy. To achieve this, I will 1) investigate the components of ANPP in different grazing intensities across varied grassland types and evaluate the accuracy of the widely used methods for ANPP model training and validating. 2) identify changes in data distributional shifts over training, testing, and deployment phases. 3) provide two approaches, distributional shifts mitigation and standing-consumed biomass combination, to improve grassland ANPP modeling accuracy. and 4) apply the developed models to quantify the ANPP underestimation at the global scale. The MSCA-European Postdoctoral Fellowship will be hosted by the University of Copenhagen (UCPH, Denmark), home to world-leading experts in spatial monitoring and analysis, and supervised by Prof. Rasmus Fensholt, a renowned expert in Remote sensing/Earth observation ecology. The results of UeNPP will contribute to a better understanding of grassland biomass dynamics and improve decision-making processes for grassland managers globally. The findings will support the development of sustainable grazing practices and contribute to climate change mitigation efforts.

Keywords: grassland aboveground net primary productivity,remote sensing,machine learning models,data distributional shift,remotely sensed phenology timings
CREATIVE DRIVERS FOR SUSTAINABLE LIVING HERITAGE LANDSCAPES (PALIMPSEST)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Culture, creativity and inclusive society
Theme: Culture, creativity and inclusive society
Destination: Innovative Research on the European Cultural Heritage and the Cultural and Creative Industries
Action type: Research and Innovation actions
Countries involved: Italy,Denmark,Spain,Slovenia,Greece,France,Poland,Czechia,Germany,Belgium
Call: The New European Bauhaus – shaping a greener and fairer way of life in creative and inclusive societies through Architecture, Design and Arts (HORIZON-CL2-2022-HERITAGE-01, 2022)
Start date: 2023-03-01
End date: 2026-04-30
Duration (months): 38.0
Total project cost (EUR): 2998897.5
EU funding (EUR): 2998897.5
Participants: 24
Project status: SIGNED

PALIMPSEST takes inspiration from the original meaning of the Greek word παλίμψηστος (palimpsestos, ‘again’ + ‘scrape’), which describes the process of the writing practices over papyrus: existing text was scraped and washed off, the surface re-smoothed, and the new literary material written on the saved material. PALIMPSEST adopts this re-writing perspective and grounds it on a living heritage approach. PALIMPSEST envisages regenerating the lost “sustainability wisdom” underlying the production of heritage landscapes through the activation of co-creation processes involving creative actors, technical stakeholders and civic society. Here architecture, design and art practices will dialogue with place-specific needs and broad systemic challenges to imagine new scenarios and experiment with innovative practices connecting human actions, landscape heritage and sustainability objectives. Such experiments will envision novel Landscape Scenarios aiming at producing dedicated Landscape Services, inspired by the generation of beneficial outcomes on ecosystem functions, which the creative contribution of CCIs will empower. Human practices will arise as relevant agents of a new sustainable palimpsest process. PALIMPSEST will integrate the aforementioned Landscape Services in environmental-sensitive solutions with sustainable finance infrastructures to support the sharing and circulating of positive externalities at different levels among the landscape service actors and communities. PALIMPSEST revolves around three pilots with strong cultural identities and relevant environmental problems: Lodz (PL), a UNESCO city of films fighting the highest air pollution levels in Europe; Milan fringes (IT), traditional agricultural landscapes struggling with unsustainable water use; Jerez de la Frontera (ES), an Andalusian wine landscape and vernacular site challenged by renewable energy production facilities.

Keywords: landscape services,living landscapes,sustainable practices,hybrid prototyping
Robotic, Automated and DIgital solutions for improving building renovAtion and New Construction Efficiency (RADIANCE) (RADIANCE)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Energy
Destination: Efficient, sustainable and inclusive energy use
Action type: Research and Innovation actions
Partnership: B4P (co-programmed)
Countries involved: Spain,Portugal,Finland,Poland,Switzerland,France,Norway,Germany
Call: Robotics and other automated solutions for construction, renovation and maintenance in a sustainable built environment (Built4People Partnership) (HORIZON-CL5-2024-D4-02, 2024)
Start date: 2025-09-01
End date: 2029-08-31
Duration (months): 48.0
Total project cost (EUR): 3999189.25
EU funding (EUR): 3999189.25
Participants: 14
Project status: SIGNED

RADIANCE is a 48-month project that aims to enhance the efficiency, safety and sustainability of the construction sector by automating the construction of new buildings and the renovation and maintenance of Europe’s buildings stock, using advanced robotics, automated and digital technologies. The main results expected in RADIANCE are (i) new robotic solutions for façade renovation, including: automation of the inspection and diagnosis workflow, including energy assessment; development of and overactuated drone platform for inspection, diagnosis and repair, capable of carrying non-destructive evaluation (NDE) probes and the pick&place of insulation panels; an NDE-multiphysics probe for evaluating concrete; a cable robot for crack repairing and anchoring to the wall of the insulation panels; and a drone for cleaning, and base coat (mortar) and finishing coat (paint) spraying; (ii) Augmented reality head-mounted display (AR HMD) for worker safety, integrating visual stimuli, pollution levels and ergonomic inputs; (iii) adapted Cooperative Intelligent Transport Systems (C-ITS) for construction (iv) improved construction site planning and their logistics, based on tactic and strategic approaches (e.g. site layout automation models, agent-based simulations); (v) an autonomous quadruped robot for monitoring, layout implementation and on-demand tool assistance; (vi) a platform for digitalizing the project and ensure data interoperability; (vi) solutions validated in 3 scenarios (renovation, human-robot interaction & safety, and construction optimization) consisting in 4 relevant environments, located in Norway, Poland and Spain, covering different EU building typologies regarding material (concrete, masonry, wood, plaster) and form & usage (e.g. residential projects, research centres).

Keywords: construction,renovation,robotics,drones,automation,digitalization,interoperability,augmented reality,quadrupedal robots,abm,c its,construction logistics and planning,energy rehabilitation