Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Disruptive AI project management solution to drastically reduce the costs & risks of European large-scale construction projects (Lili.AI)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Open to supporting projects in all areas of science, technology,...
Action type: EIC actions
Countries involved: France
Call: EIC Accelerator Open 2021 (HORIZON-EIC-2021-ACCELERATOROPEN-01, 2021)
Start date: 2022-06-01
End date: 2025-05-31
Duration (months): 36.0
Total project cost (EUR): 3702291.25
EU funding (EUR): 2591603.88
Participants: 1
Project status: SIGNED

Lili.AI is a breakthrough AI project management solution for organizations executing large-scale (€250M+ budget) projects in infrastructure construction, engineering, waste management & other industries. Lili.AI combines unique AI models trained on real-life large-scale project management data with award-winning natural language processing technology. It extracts actionable insights from textual unstructured datasets —emails, meeting minutes, memos, reports, etc. Our TRL6 tech has proven successful in claims litigation with Lynkea; we are currently trialling the advanced prototype with NGE. At TRL9 our solution will register weak signals received through real-time monitoring of project documentation. It will address organizations’ inability to detect & address problems early, which leads to their aggravation & costly penalties (average claim is ~€54M globally, according to Arcadis). Lili.AI will help them avoid such expensive delays/failures.

Keywords: big data,construction,infrastructure,project management,construction engineering,large scale projects,risk management,claim litigation
Accelerating global EV transitions: Understanding the influence of institutional context (EVtrans)

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: Social Sciences and Humanities
Countries involved: United Kingdom
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2026-02-01
End date: 2028-01-31
Duration (months): 24.0
Total project cost (EUR): 260347.92
EU funding (EUR): 260347.92
Participants: 1
Project status: SIGNED

A global electric vehicle (EV) transition is underway to curtail carbon emissions, and many countries have adopted policies to promote electric vehicles (EVs). Often, countries show variations in their EV policies and EV penetration because of variations in the feedbacks between policy, politics, and technology due to differences in national politico-economic contexts. Also, countries adopt successful policies of other countries. However, a systematic understanding is lacking of a) why countries differ in their EV policies and EV transitions, b) how feedbacks operate in different countries, and c) how effective similar policies are in different countries. EVtrans answers these questions through comparative analysis of feedbacks in Germany, the UK and India and producing a novel interdisciplinary analytical framework. It combines insights from political economy, transition studies, and policy studies. Methodologically, it uses documentary analysis and semi-structured interviews for data gathering and process tracing and thematic analysis for analyzing data. Recently, the EU adopted a common ‘fit for 55’ legislation, which mandates that from 2035, all new cars in the EU must be zero-emission vehicles, and EVs will play a crucial role here. However, the EU member countries differ widely in their politico-economic context. EVtrans’ framework would help examine how such a common legislation will create feedbacks between policy, politics, and technology in different EU members. This would be useful in assessing the effectiveness of ‘fit for 55’ and taking corrective policy measures. To ensure the project’s successful implementation, I will conduct my research at the University of Sussex, School of Global Studies, under Prof. Peter Newell, who is a global authority on the political economy of energy transitions. This project will strengthen my academic profile and help me get a tenured position.

Keywords: transition studies,energy transition,policy feedback,electric vehicles
EXplainable ALgorithmic Tools (EXALT)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Countries involved: Poland
Call: ERC PROOF OF CONCEPT GRANTS2 (ERC-2022-POC2, 2022)
Start date: 2023-09-01
End date: 2025-02-28
Duration (months): 18.0
Total project cost (EUR): 161859.3
EU funding (EUR): 150000.0
Participants: 2
Project status: CLOSED

Deploying algorithmic solutions in real-world applications raises two challenges. First, we need easy-to-use and universal algorithms. Second, we need to guarantee that algorithmic solutions can be understood by people using them. We address the first of these challenges in TUgbOAT project, which aims to deliver unified algorithmic tools. Here, we propose to develop tools that would address the second of these challenges. In many use scenarios, algorithms propose a solution to a human operator. The main challenge in such cases is to convince him to use the returned solution. Traditionally we think of algorithms in a black-box manner, i.e., as a tool to find a good solution. We do not expect algorithms to give a human-understandable explanation of why this is the best solution, or what alternatives exist or what are the bottlenecks. Nevertheless, we humans still tend to ask these questions even if we understand the algorithms that are used. Currently, we lack good tools that could explain the results of optimization algorithms, e.g., for the assignment problem. For practitioners, like ourselves, that work together with companies to deploy algorithmic solutions in real-world cases, the need to provide explainable algorithms becomes immanent. Here we will test and implement results developed in TUgbOAT that can be used to complement the algorithms with human explanations. In particular, we plan to: - enrich algorithms to give meaningful alternative solutions, - apply Shapley value methods to determine key solution elements, - work with perturbed inputs to create robust and more concise solutions, - generate concise decision trees that would explain steps taken by algorithms. This project aims to deliver the base parts of a software library that would give explainable algorithms. We plan to concentrate on the task assignment problem (i.e., matchings) where we already cooperate with companies.
adVANced technoloGical solutions coupled with societal-oriented Understanding and AwaReness for Disrupting trafficking in human beings (VANGUARD)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Civil Security for Society
Theme: Civil Security for Society
Destination: Fighting Crime and Terrorism
Action type: Innovation actions
Countries involved: Greece,Germany,Spain,Italy,Poland,Romania,Portugal,Moldova, Republic of,Estonia,United Kingdom,Belgium,France
Call: Effective fight against trafficking in human beings (HORIZON-CL3-2022-FCT-01, 2022)
Start date: 2023-11-01
End date: 2026-10-31
Duration (months): 36.0
Total project cost (EUR): 4862445.0
EU funding (EUR): 4536682.5
Participants: 23
Project status: SIGNED

VANGUARD aims to strengthen the fight against trafficking in human beings (THB) at the nexus of advanced technological solutions, understanding, awareness raising, and training in order to disrupt the trafficking chain at an early stage and address the culture of impunity. In particular, VANGUARD aims to provide an improved intelligence picture of THB, with particular focus on THB for purposes of sexual exploitation, labour exploitation, and forced criminality. This will be achieved through developing a modular and trustworthy suite of tools for detecting, identifying, investigating, and preventing online-facilitated THB activities and THB-related activities at (border) checkpoints based on the analysis of online multimedia content and multimodal streams, by leveraging the latest advancements in Artificial Intelligence (AI). It will also address the societal dimensions of THB through improved understanding and engagement with relevant actors, improved strategies of international cooperation, increased awareness, and better evidence-based policymaking. The capacity of end users (including Police and Border Guard Authorities) will be further enhanced and will enable them to tackle such criminal activities in an effective manner based on advanced tools and solutions and on innovative training curricula. VANGUARD will be validated in field tests and demonstrations in three operational use cases. Extensive training, hands-on experience, joint exercises, and training material will boost the uptake of VANGUARD tools and technologies. With a Consortium of seven Police and Border Guard Authorities (including from countries of origin and transit of THB networks and countries neighbouring Ukraine), one Police Academy, eight research/academic institutions, four industry partners (including two SMEs), and two CSOs, VANGUARD delivers a strong representation of the challenges, requirements, and tools to meet its objectives.

Keywords: intelligence picture,technological solutions,ai,computer vision,multimodal analytics,awareness raising,serious games,policy recommendations
Neural Circuits for Error Correction (ECoFly)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator 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: Portugal
Call: ERC CONSOLIDATOR GRANTS (ERC-2022-COG, 2022)
Start date: 2024-01-01
End date: 2028-12-31
Duration (months): 60.0
Total project cost (EUR): 1999970.0
EU funding (EUR): 1999970.0
Participants: 1
Project status: SIGNED

To survive in natural habitats, animals move through space according to their goals. However, the uncertainties of the environment, alongside inevitable variations in neuromuscular signals, change the context in which a walking step occurs, leading to unintended movement. Thus, task performance can be jeopardized if the erroneous action is not rapidly corrected based on current posture and behavioral goals. How these aspects of control functions are implemented and coordinated across the Central Nervous System remains unknown. Here we propose studying the circuits involved in monitoring our movements, since they are key intermediaries between motor planning and posture-dependent execution. Using the compact brain of the fly Drosophila melanogaster, we will ask two fundamental questions: how is neural activity distributed across multiple networks integrated to estimate self-motion? How is this internal estimate used to correct erroneous movement? Using a self-paced behavior, in which a fly drifting from a stable heading turns based on an internal drift estimate, we have found a circuit sensitive to angular velocity, richly interconnected to the fly’s analogue of the spinal cord and higher-order brain areas, and critical to drift-based turns. We will leverage these results, and combine them with electron microscopy, behavior, physiology, optogenetics and modelling to study circuit mechanisms for course correction. We will: 1) use connectomics and manipulations of neural activity to identify pathways involved in corrective turns, 2) record from the identified neurons and correlate their activity with behavior, 3) perturb cell type-specific neurons to test their role on self-motion computations and on corrective turns, and 4) test neural activity in different behavioral contexts. These experiments will establish unprecedented causal relationships between neural computations and movement and reveal the functional organization of distributed circuits for walking control.

Keywords: motor control,drosophila,circuits,proprioception,corollary discharge signals,internal models,steering,locomotion
Towards an Understanding of Artificial Intelligence via a transparent, open and explainable perspective (TUAI)

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: Poland,Italy,Spain,Norway,Germany
Call: MSCA Doctoral Networks 2023 (HORIZON-MSCA-2023-DN-01, 2023)
Start date: 2024-10-01
End date: 2028-09-30
Duration (months): 48.0
Total project cost (EUR): 3358980.0
EU funding (EUR): 3358980.0
Participants: 14
Project status: SIGNED

The focus of the TUAI project will be on the highest quality doctoral training on sustainable the AI that is used in smart manufacturing, smart cities, smart healthcare and smart mobility. This will be achieved by multilateral academic and industry trainings for a new generation of creative, entrepreneurial and innovative researchers – Doctoral Candidates (DCs), who will be able to face both the current and future challenges that are associated with the development of Machine Learning (ML) and who will be able to effectively use the latest advances in artificial intelligence and data mining in order to ensure the competitive advantage of the European producers that will use the practical solutions from the TUAI project. The project will focus on ML-based smart services that should first meet the customers’ needs but with the same importance should focus on protecting the natural environment by creating smart services dedicated for smart mobility, to reduce energy consumption by smart cities and to avoid the losses and suboptimal use of resources in smart manufacturing. The scope of the research will be organised under four research areas (RA): (i) Time Series Analysis, (ii) Sensor Fusion, (iii) Federated Learning and (iv) the sustainability and trustworthiness of the AI solutions. The precise challenges for each RA will be expressed by research questions (RQ), which will be resolved by the individual DC projects. The RQs will not be separate areas of research but will be complementary and intertwined components. Therefore, it is not sufficient to train DCs in one RA and ignore another. The TUAI project will provide holistic research training through secondments and network-wide training activities. The proposed approach will ensure excellent research training for the thirteen DCs who will be prepared for future work both in academic and non-academic sectors and will be able to face the challenges that are associated with the different application areas.

Keywords: time series analysis sensor fusion federated learning sustainability and trustworthiness of ai solutions
Implementation of novel ultrapermeable membranes for biomethane production (Mem4BIOCH4)

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: Spain,Netherlands
Call: MSCA Postdoctoral Fellowships 2021 (HORIZON-MSCA-2021-PF-01, 2021)
Start date: 2023-07-01
End date: 2025-06-30
Duration (months): 24.0
Total project cost (EUR): 165312.96
EU funding (EUR): 165312.96
Participants: 4
Project status: CLOSED

Biogas from the anaerobic digestion of organic waste and wastewater is regarded as one of the most promising renewable energy vectors to partially address the current global environmental and energy challenges. However, CO2 and H2S, along with other biogas pollutants, need to be removed to enlarge the scope of biogas applications. Membrane technology is currently undergoing a rapid development driven by the synthesis of new polymer materials with promising properties and enhanced performance, and is set to play an increasingly important role in reducing the environmental impact and cost of industrial biogas upgrading. Mem4BIOCH4 aims at fostering industrial biogas upgrading by engineering low-cost and efficient membrane modules based on innovative polymer materials with superior gas separation performance in order to enhance biogas valorisation via production of high-quality biomethane. Mem4BIOCH4 will focus on overcoming the current limitations of membranes in order to support a low-cost upgrading of raw biogas via CO2 and H2S separation using novel polymer materials. In addition, emphasis will be also put on the CO2 and N2 separation from biogas produced in the anaerobic digestion of domestic wastewater, which has not been assessed to date to the best of our knowledge. This multidisciplinary action will have a significant impact in fields such as chemistry (novel monomers with specific structural features), materials science (new polymers with enhanced properties), environmental technology (more sustainable biogas conversion technologies), and economy (low-cost biogas upgrading fostering the biomethane market). Mem4BIOCH4 will bring multiple environmental and economic benefits to society, facilitating the creation of a sustainable and cost-competitive biomethane industry in Europe. This action will be carried out at Valladolid University, with secondments at University of Twente and Biothane (Veolia Water Technologies).

Keywords: biogas upgrading biomethane gas separation membranes ultrapermeability polymer membranes
Transforming everyday surgery into a painless and scarless experience (M.I.L.A.N.O. Robot)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Open to supporting projects in all areas of science, technology,...
Action type: EIC actions
Countries involved: Italy
Call: EIC Accelerator Open (HORIZON-EIC-2022-ACCELERATOR-01, 2022)
Start date: 2023-04-01
End date: 2025-09-30
Duration (months): 30.0
Total project cost (EUR): 4031750.6
EU funding (EUR): 2499999.0
Participants: 1
Project status: SIGNED

M.I.L.A.N.O. Robotic Platform is composed of a Camera Robot and Assistive Robot that can be used independently or combined with operative arms as Operative Robot. Robots are compact and easily portable, thus, there is no need for a dedicated surgical room unlike with current large size robots. The initial set up can be done in only 10 minutes due to the use of single-use tools and easy configuration. The versatility of the Operative Robot to switch between single port (access via one incision in the abdomen) and multiport (access via multiple incisions in the abdomen) surgeries enable to use the same robot for a number of surgical specialties, such as gastrointestinal, gynecology and urology. Incorporated lens cleaning and smoke evacuation system offer a clear vision and increase the safety of surgical procedures. The competitive purchase price and cost per procedure offered to hospitals via convenient purchase plans allow to increase patients’ access to minimally invasive procedures.

Keywords: minimal invasive surgery,configurable,scalable,flexible,custom,modular,compact,robotic techniques,roboscopic techniques,transportable
Provenance And tranSport PathwayS of mArine proxy-bearinG particlEs (PASSAGE)

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: Spain
Call: ERC STARTING GRANTS (ERC-2021-STG, 2021)
Start date: 2022-09-01
End date: 2029-02-28
Duration (months): 78.0
Total project cost (EUR): 1499766.0
EU funding (EUR): 1499766.0
Participants: 2
Project status: SIGNED

Understanding the nature and pace of climate change in the future requires robust and accurate information on past Earth's climate changes. Much of what we know about past short-term climate variability stems from marine sediment sequences, whose sedimentary components provide an ever-expanding array of paleoclimatic proxies. The fundamental assumption underlying most paleoceanographic investigations is that the environmental signal encapsulated in marine sedimentary components reflects that of the overlying water column at the time of formation. However, evidence indicates asynchronous synthesis and transport from distal locations for co-deposited sediment constituents. Consequently, fundamental questions arise regarding the fidelity of paleoclimate records. Moreover, the magnitude of such temporal and spatial offsets likely varies in concert with hydrographic ? and associated hydrodynamic - changes. Here, I outline a research program to directly assess the impact of hydrodynamic processes on marine particles and explore implications for paleoceanographic studies. Specifically, this research will: (i) estimate the contribution of allochthonous/asynchronous material; (ii) constrain the extent and nature of the specific modes of particle transport; (iii) quantify their potential to bias derived climate signals; (iv) correct for aliased proxy records and reconstruct past hydrodynamic changes. This work would provide a crucial advance in our ability to reliably interpret sub-millennial scale climatic signals. We will develop cutting-edge methodologies for particle isolation and will apply state-of-the art analytical techniques for compound-specific radiocarbon (14C) determination. The timeliness of this project is fostered by the recent advances in 14C detection for measurement of ultra-small samples. Equipped with the required theoretical grounds and analytical expertise, I am ideally positioned to effectively execute this challenging project.

Keywords: radiocarbon