Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Spin-obit torque heterostructures based on topological insulators and 2D materials (SORTIR)

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: Physics
Countries involved: Spain,France
Call: MSCA Postdoctoral Fellowships 2022 (HORIZON-MSCA-2022-PF-01, 2022)
Start date: 2023-04-01
End date: 2025-03-31
Duration (months): 24.0
Total project cost (EUR): 165312.96
EU funding (EUR): 165312.96
Participants: 2
Project status: CLOSED

In recent years, spintronics has seen a substantial increase on application in data storage devices, targeting low-energy cost and non-volatility. Indeed, the magnetic random access memory (MRAM) is the only non-volatile memory capable of high-density, high-endurance and fast-write operation and is considered as the best candidate for embedded non-volatile applications. In this technology, the information is stored by the direction of the magnetization of ferromagnets. The most scalable and efficient way to control the magnetization is the use of spin currents, which exert a torque on it and can fully reverse its direction. The most common spin-sources investigated for spin-orbit-torque MRAM (SOT-MRAM) are heavy metals such as Pt, W or Ta, which convert an electrical current in a spin current by means of, for example, the spin Hall effect arising from their strong spin-orbit coupling. The reported SOT efficiencies indeed allow current-induced magnetization switching, however, the current densities required are still too large for practical implementations. Steady progress in two-dimensional materials (2DMs) offers new perspectives for downscaling and improving MRAM performance. Their 2D nature and weak van-der-Waals interaction between layers enables to create atomically thin stacks with sharp interfaces, circumventing roughness and inter-diffusion, which significantly degrade the spin properties of conventional materials. Among them, topological insulators and transition metal dichalcogenides are expected to be very efficient spin-sources that could significantly enhance the SOTs in comparison to conventional bulk materials. The SORTIR project therefore aims at using the unique properties of 2DMs to unleash their potential for high SOT efficiencies, targeting both low-consumption and high density for data storage.

Keywords: spin orbite torque,graphene,spinterfaces,topological insulators,tmdc
Chemical Transformations in the Inter-Stellar Medium: Photochemical Processes (AstroMol)

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: Israel
Call: ERC STARTING GRANTS (ERC-2023-STG, 2023)
Start date: 2023-10-01
End date: 2028-09-30
Duration (months): 60.0
Total project cost (EUR): 1461701.0
EU funding (EUR): 1461701.0
Participants: 1
Project status: SIGNED

Until the beginning of the last century, the scientific convention was that the material in the interstellar medium (ISM) between stars and galaxies was mostly in atomic and ionic form – thus the formation of complex molecules was improbable. Today, however, large complex molecules are being discovered at a dazzling rate – demonstrating the molecular nature of our universe. The physical conditions in the ISM are extremely different than on Earth: in addition to extreme temperatures and low density, there are radiation fields, which, when interacting with atoms and molecules, result in unique chemistry occurring in excited electronic states. This interaction is responsible for many of the chemical phenomena observed in the ISM. Currently, very little is known about the formation mechanisms of molecules in the ISM. The efforts to uncover the chemistry of the ISM are multidisciplinary, yielding experimental, observational, and theoretical results. Theoretical results are crucial for obtaining a molecular-level understanding of chemical phenomena – theoretical results help to guide and decipher experimental and observational results. However, up to now, the toolkit of modern quantum chemistry cannot model the dynamic of large molecules in a highly excited electronic state. The aim of this proposed study is to fill this gap: I will develop new theoretical capabilities within quantum chemistry (specifically, ensemble density functional theory) that will enable us to model the dynamic of highly excited large molecules such as polyaromatic hydrocarbons (which play a crucial role in the chemical evolution of the ISM). The ability to accurately model the dynamics of excited states will significantly advance the field of computational chemistry, giving it the ability to model systems that are currently outside its reach – and provide a leap in our current understanding of the chemistry of the ISM.

Keywords: astrochemistry,theoretical chemistry,computational chemistry,photochemistry,ab initio molecular dynamics,excited state dynamics,static electron correlation,density functional theory
Digital Innovations and Diagnostics for Infectious Diseases in Africa (DI-DIDA)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Health
Theme: Health
Destination: Tackling diseases and reducing disease burden
Action type: Research and Innovation actions
Countries involved: France,United Kingdom,Kenya,Netherlands,Senegal,Tanzania, United Republic of,Uganda,Italy
Call: Innovative approaches to enhance poverty-related diseases research in sub-Saharan Africa (HORIZON-HLTH-2021-DISEASE-04, 2021)
Start date: 2022-10-01
End date: 2027-09-30
Duration (months): 60.0
Total project cost (EUR): 6468604.28
EU funding (EUR): 6468604.28
Participants: 16
Project status: SIGNED

The DI-DIDA project's overall objective is to tackle poverty-related infectious diseases in sub-Saharan Africa and African islands by i) strengthening the African research capacities, ii) enhancing technology development in diagnostics and digital technologies in Africa, and iii) encouraging adoption of innovations by sub-Saharan health authorities, clinicians, businesses, and patients. For that, the consortium includes leading partners from four sub-Saharan African countries (Kenya, Senegal, Tanzania, and Uganda), one from African Islands (Réunion), and the EU. To reach our objectives, we will implement a combination of complementary actions: - Joint research projects on infectious diseases co-morbidities, human-livestock interactions, infectious diseases diagnostic technologies, digital health solutions for Africa, and socio-economic factors of innovation adoption by African people. - The co-development of an innovative low-cost diagnostic and digital/mHealth decision support technology and its integration into existing African digital health infrastructure. - Clinical trials. - A PhD fellowship for students in Africa with joint PhD awarding by EU counterparts. - Research capacity building (management, grant writing, publications etc.) - Staff exchanges. - Training. - Organisation of citizen and brokerage events targeting businesses and policy makers. - A comprehensive dissemination, exploitation, and communication action plan. The impact of this project will be: - Improved research / technology development and manufacturing /clinical trials capacities in sub-Saharan Africa. - New knowledge gained on several topics related with poverty-related infectious diseases. - Increased international networks and reinforcement of the collaboration between Africa and the EU. - A new low-cost diagnostics and decision support technology developed and its adoption fostered by dedicated studies and collaboration with health authorities. - Better informed sub-Saharan populations about infectious diseases.

Keywords: infectious diseases,non communicable diseases,co morbidities,diagnostics,lateral flow test,mhealth,digital health,human livestock interactions,public and patient involvement and engagement
Dynamic Selection and Configuration of Black-box Optimization Algorithms (dynaBBO)

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: France
Call: ERC CONSOLIDATOR GRANTS (ERC-2023-COG, 2023)
Start date: 2024-10-01
End date: 2029-09-30
Duration (months): 60.0
Total project cost (EUR): 1999975.0
EU funding (EUR): 1999975.0
Participants: 1
Project status: SIGNED

Black-box optimization algorithms are among the most widely applied optimization techniques in practice, used to solve numerous problems across a broad range of industrial branches and academic disciplines every day. Given this importance, it is not surprising that a plethora of different black-box optimization algorithms exist, complementing each other in strengths and weaknesses. In the dynaBBO project, we set out to obtain more efficient black-box optimization techniques by leveraging this complementarity, both with respect to different problem instances and with respect to different stages of the optimization process. To this end, we will develop approaches that select and dynamically switch between different black-box optimization algorithms "on the fly". The two key research questions that guide our project are (1) when to switch from one algorithm to another, and (2) how to warm-start the selected solver so that it can continue the search as effectively as possible. Both questions are largely under-explored and are handled rather naively in practice. To obtain our dynamic approaches, we intertwine insights about black-box optimization algorithms, obtained through rigorous theoretical analyses, with automated machine learning techniques. In particular, we will design trajectory-based algorithm selection and configuration techniques that combine exploratory landscape analysis with newly designed algorithm features that capture information about the solver-instance interaction. We compare the efficiency of these feature-based approaches with deep learning techniques, reinforcement learning, and approaches based on hyperparameter optimization. We will further increase our project's impact by validating its results on applications in bio-medicine and in computational mechanics.

Keywords: black box optimization,randomized search heuristics,evolutionary computation,automated machine learning,algorithm selection,algorithm configuration,local search,discrete optimization,algorithm analysis,randomized algorithms,parameter control
Zambezian Entanglements in the South Central African Iron Age (ZAMBEZI)

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: United Kingdom,Zambia
Call: ERC ADVANCED GRANTS (ERC-2024-ADG, 2024)
Start date: 2026-01-01
End date: 2030-12-31
Duration (months): 60.0
Total project cost (EUR): 2813408.0
EU funding (EUR): 2813408.0
Participants: 2
Project status: SIGNED

ZAMBEZI introduces a radical new precolonial history of an African region in the Middle Zambezi Valley. The project explores the site of Ingombe Ilede in Zambia and its regional setting, using a nested methodology offering three levels of innovation: (1) deep understanding of a crucial but under-explored site; (2) pioneering methodologies offering unique detail on human settlement and mobility; and (3) a transformative view of the African past derived from an exploration of diversity and contingency. Rescue excavations at Ingombe Ilede in 1960 captured attention due to the discovery of rich burials suggesting the existence of a wealthy elite; the materials illuminate resource networks from the Copperbelt to the eastern African coast. The richest period was contemporary with 15th/16th century Portuguese colonial intrusion, and thus Ingombe Ilede has a story to tell that encompasses the socio-economic landscape of the precolonial Zambezi, as well as the emergence of a local elite during a moment of global encounter. Building on the expertise of the PI in constructing human-scale archaeological histories through combined methods, the project will apply an ambitious methodology combining established techniques and new technologies to create detailed life histories and diachronic understandings of settlement and society. The project will produce a microhistory with the capacity to transform our understanding of macro-histories of this region, highlighting emergent cultural forms that stem from mobility and encounter. Detailed, human-level stories about African societies are urgently needed, as the building blocks of our explorations of the global past. ZAMBEZI offers a unique opportunity to tell a new story about the African past, beyond narratives of migration or the emergence of single central places, focusing rather on the histories of local societies and economies, craft producers, and the way they forged forms of power and status on the eve of colonial expansion.

Keywords: african archaeology global entanglements zambezi zambia portuguese colonialism
Virus Inhibition by siRNA Optimized by NMR (VISION)

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: France
Call: ERC PROOF OF CONCEPT GRANTS2 (ERC-2022-POC2, 2022)
Start date: 2022-09-01
End date: 2024-02-29
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: CLOSED

The last two years, profoundly marked by the COVID-19 sanitary crisis, have demonstrated the difficulties to answer adequately to the emergence of novel pathogenic viruses. Today, no broad-spectrum antiviral exists similar to antibiotics targeting bacteria. Repositioning of existing molecules had limited success despite intense initial hopes. Vaccines have played a major role in fighting the pandemic and limiting the impact of the virus, however it remains insufficient to end the pandemic, due to many factors including their worldwide uneven accessibility, their intrinsic efficiency towards different variants and the complex socio-political context related to mass vaccination. There is therefore an urgent need of novel approaches to design molecules targeting viruses and in particular SARS-CoV-2. To answer this challenge, we propose a novel strategy derived from fundamental research on small interfering RNA (siRNA). This project derives from the ERC Starting Grant PARAMIR, in which novel structural biology approaches are proposed to understand the mechanism of recognition of a similar class of RNA involved in numerous diseases, especially cancer. Our approach combines the latest advances in SARS-CoV-2 virology and structural biology to propose efficient and specific molecules active towards a broad range of SARS-CoV-2 current and future variants. If successful the project will lead to a class of siRNA, with optimized specificity and stability, validated in vitro and ex-vivo in a reconstituted human airway epithelial model, and ready for testing towards pre-clinical and clinical stage. Finally, the strategy proposed here will be on a longer term applicable to multiple other pathogenic viruses.
The Lockdown Cohort-Effect: Does the unequal impact of the COVID-19 pandemic on fertility lead to long-term consequences for health and public services provision? (LoCo-Effect)

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: Finland
Call: MSCA Postdoctoral Fellowships 2023 (HORIZON-MSCA-2023-PF-01, 2023)
Start date: 2024-07-01
End date: 2026-06-30
Duration (months): 24.0
Total project cost (EUR): 199694.4
EU funding (EUR): 199694.4
Participants: 1
Project status: SIGNED

The COVID-19 pandemic placed a heavy social and health burden on populations. Yet, because of social inequalities in the pandemic’s impact on fertility behaviour, babies conceived during the pandemic may be the most socially advantaged and healthiest birth cohort of the last decades. Research on this counterintuitive but realistic consequence of the pandemic – the Lockdown Cohort (LoCo)-Effect – is critical for public policy and the understanding of pandemic consequences. As individual-level register data for babies conceived and born in 2021 and later is only just becoming available, we still know little about how the pandemic changed the composition of parents and to what extent this explains changes in birth outcomes for those born during the pandemic. To address this knowledge gap, I will use individual-level register data (2010-2022) from Finland, Scotland, Austria, Spain, the United States, and Brazil and natural experimental methods to study the LoCo-effect comparatively. This strengthens the generalisability of results across different welfare regimes and pandemic experiences. First, I will study the magnitude of compositional changes in parental characteristics due to the unequal impact of the pandemic on fertility. Second, I will analyse the extent to which differences in prematurity and birth weight between the LoCo and earlier cohorts are caused by this change in parental characteristics. Third, I will estimate how the pandemic has affected intersectional social inequalities in birthweight and preterm birth. This project is innovative because it bridges disconnected debates in demography, epidemiology, and sociology to explain why, against expectations, pregnancy and birth outcomes have improved during the pandemic. As parental characteristics and birth outcomes shape health, developmental, and socioeconomic outcomes throughout life, the results of this project will influence social and public health research and policy for decades to come.

Keywords: register based individual level data comparative research,social epidemiology health inequalities birth outcomes natural experimental methods
Foundation models for molecular diagnostics - machine learning with biological ‘common sense’ (FoundationDX)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Integrative B iology : from Genes and Genomes to S ystems
Panel description: For all organis ms: Genetics, epigenetics, genomics  and other ‘omics studies , bioinformatics, systems biology , genetic diseases , gene editing, innovative methods and modelling,  ‘omics for personalised  medicine
Countries involved: Netherlands
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): 2000000.0
EU funding (EUR): 2000000.0
Participants: 1
Project status: SIGNED

Molecular diagnostics is crucial in fulfilling the promise of personalized medicine. While we are amidst an AI revolution, current machine learning models (ML) struggle to effectively learn from molecular (‘omics’) patient profiles and fail to make robust predictions. Perhaps this is not a surprise. After all, molecular disease biology is immensely complex, and we ask ML models to predict such complicated things as patient prognosis, without them ‘knowing’ anything about molecular biology and based on limited training data. To address this, I will create foundation models on top of the vast troves of available biomolecular data, such as multi-omics profiles in healthy and diseased tissues, high-resolution single-cell data and biological knowledge graphs. This unique approach is driven by self-supervised learning (SSL), an important driver of AI, which offers the opportunity to learn a comprehensive representation of the multimodal biology of the cell – without the need for well-annotated patient data. Starting from this strong basis, the FoundationDX model can then reliably predict cancer subtype or prognosis as it no longer needs to start from scratch on too high-dimensional, too low sample-size datasets. Effectively, we give our systems biological ‘common sense’, foregoing the need for millions of labeled training samples. This uniquely enables us to address one of the most clinically relevant questions: which treatment is best for the patient? The FoundationDX research program is designed to deliver key insights into how the SSL revolution can be used to drive progress in the field of molecular diagnostics. It contains a ‘clinical-grade’ benchmarking module and solves three urgent diagnostic challenges, including noninvasive subtyping of pediatric brain cancer. The time for powerful, robust and generalizable, knowledge-aware machine learning solutions to previously intractable molecular diagnostics problems has come. FoundationDX aims to deliver this.

Keywords: multi omics data integration,molecular diagnostics,machine learning for omics data,transfer learning,self supervised learning,representation learning