Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : PERENNIAL (Horizon Europe, appel 2022, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Université de Lund — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2614373.0 €

PERENNIAL will investigate whether a shift from annual to perennial grain crops as the basis for food production is possible, and identify the major opportunities and obstacles for such a “perennial revolution”. Since the Neolithic Revolution the world food system has been dominated by annual crops (cereals, legumes, and oil seeds) covering 70% of global croplands. The use of annual plants disrupts agro-ecological processes every season, resulting in soil erosion, nutrient losses, and greenhouse gas emissions from machinery and soils. The costly practices of managing annuals, including a strong dependence on herbicides and mineral fertilisers, partly explain the high indebtedness and low returns in agriculture. A shift from annual to perennial grains that are planted once and harvested for many consecutive years, could revolutionise agriculture and revitalise rural society. It could solve problems associated with annuals and increase the potential for adaptation to climate change. Advances in plant breeding have shown that a shift to perennials is possible within a few decades, but it would challenge powerful vested interests. To investigate the prospects of a perennial revolution, we will: a) conduct a systematic critique of the current political economy of agriculture, b) suggest desirable, viable, and achievable perennial alternatives to annual monocultures, and c) develop a strategy for change. We complement social science expertise with that of plant breeding, agro-ecology, soil, and plant science. Social Fields Theory is applied to study the political economy of agriculture including the politics of seeds and agrochemicals and Soil Health theory to suggest alternatives. We then incorporate ecological feedbacks to develop an Agricultural Treadmill Theory and apply Transition Theory to study social change. Using mixed methods, we will conduct discourse analysis, network analysis, and modelling at multiple scales including farms, river basins, and jurisdictions.

Mots-clés : agricultural change domestication mitigation of climate change perennial grains political economy real utopias rural economies
Projet ERC : FINANCEforALL (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Université nouvelle de Lisbonne — Pays : Portugal — Rôle : PI
Acronyme de l'entité : UNL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2498000.0 €

Understanding and managing personal finances is crucial for promoting individuals’ financial and economic well-being. Yet, financial literacy remains low globally, with only one-third of people understanding basic financial concepts. In addition, socioeconomic and gender financial literacy gaps persist, exacerbating social inequalities. Increased complexity of financial markets and personal responsibility due to longer life expectancy, the emergence of private pension plans, and the digitalization of banking underscore the need for national strategies to promote financial literacy. Those can empower individuals, enhance financial stability, and promote inclusive economic development.  FINforALL will evaluate the impact of a large-scale financial literacy intervention on the working population in Portugal, a developed country with below-average financial literacy. It investigates how financial education shapes individuals’ financial knowledge and behavior and how these effects extend to co-workers, firms, and local economies. While financial literacy programs have been studied globally, this project analyses often-overlooked spillover effects, which are important to designing cost-effective interventions.  The experimental design involves three randomization units: municipalities, firms, and employees. It combines survey data collected during the experiment with unique administrative data at the individual level (credit registry, tax filings, property registry, and private banks’ transactions) and firm-level (financial statements and matched employee-employer). These data allow me to examine the short- and long-term effects on individual outcomes, such as assets, credit, income, consumption, saving, employment, entrepreneurship, and broader effects on firms and local economies. FINforALL will be the first to examine the direct and spillover effects of financial education at the workplace, offering new insights into its broader implications of financial education.

Mots-clés : financial education financial literacy financial decisions rct firms local economies economic growth
Projet ERC : TIMESERIESFOREC (Horizon Europe, appel 2022, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Université Pompeu Fabra — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UPF
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 859950.0 €

The environment we live in is both complex and time varying. Examples of recent instabilities include the recent financial crises as well as the more recent COVID-19 pandemic and the war in Ukraine, which have substantially altered our world. Currently, however, the way researchers implement forecasts as well as the way they estimate the effects of economic policies is based on methods that either impose restrictive assumptions on the nature of instabilities or quickly become computationally demanding in the presence of instabilities.  This project proposes to develop a local projection-based estimator for time-varying parameter models and their impulse response functions in unstable environments, which we refer to as the “time-varying parameter local projection” estimator (or “TVP-LP” in short). The proposed estimator is expected to provide a feasible approach to conveniently and flexibly estimate economic models in unstable environments as well as, more broadly, forecasting and assessing the effects of economic policies. The proposed estimator can be generalized to include instrumental variables as well as be applied in vector autoregressive models with external instruments while being robust to the presence of instabilities. The proposed methodology is expected to have widespread applicability given the increasing interest in using convenient local projection estimators and the substantial evidence of instabilities in macroeconomic models.  In contrast to conventional time-varying parameter VAR models, the proposed time-varying parameter local projection estimator is robust to the presence of non-invertibility due to omitted variables and misspecification and its estimation is less computationally challenging.  The advantages of the methodology will be illustrated in terms of forecasting ability as well as the evaluation of the effects of economic policy (in particular, fiscal policy).

Mots-clés : time series econometrics,forecasting,macroeconometrics
Projet ERC : MYCiMAX (Horizon Europe, appel 2023, Advanced grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : CERCA Institution — Pays : Espagne — Rôle : PI
Acronyme de l'entité : CERCA;I-CERCA
Type d'entité : Recherche
Financement ERC de l'entité : 2499904.0 €

Even though the MYC oncogene is a “most-wanted” target in cancer therapy, no MYC inhibitor has yet reached clinical approval. The applicant has developed Omomyc, the first MYC-inhibiting mini-protein to have successfully completed a Phase Ia clinical trial. The goal of this proposal is to maximise the use of this compound, as both a therapeutic and study tool, opening new lines of research in different aspects of MYC biology. To start with, since Omomyc cannot efficiently cross the blood brain barrier, excluding it from use in brain malignancies or brain metastases, in Aim 1, we propose to validate its efficacy when delivered intracranially by different means, including osmotic pumps and hydrogels, as well as to test its delivery by intracarotid injection, in glioblastoma and brain metastases. Then, in Aim 2 and 3, we will explore its combination with personalized medicine, namely PARPi and KRASi, respectively. These 2 aims are derived from two pillars of MYC biology: its role in DNA damage response and the paradigm of oncogene cooperation. Aim 2 will allow us to shed light on the role of MYC in homologous recombination and resistance to PARPi, while Aim 3 will deliver on the molecular mechanisms underlying the cooperation of RAS and MYC in multiple tumour types, where their combined inhibition could represent an unprecedented therapeutic opportunity. Finally, Aim 4 will focus on the characterisation of cancers such as Small Cell Lung Cancer and Gastrointestinal Stromal Tumours that have the peculiarity of being defective for MAX – MYC’s natural partner – but that recapitulate a MYC-dependent tumour phenotype, likely driven by other members of the MYC network, whose function could also be hindered by Omomyc treatment. Notably, each of the aims explores new aspects of MYC biology, tracing new lines of research around the most deregulated oncogene in human tumours as well as having immediate translational applicability in upcoming clinical trials of Omomyc.

Mots-clés : myc,dna damage,glioblastoma,kras,max,sclc,gist,parp
Projet ERC : HYPER (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : Université technique de Munich — Pays : Allemagne — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 978296.25 €

Observable hadronic matter is mostly composed of u, d quarks. The strangeness degree of freedom is expected to play an important role in dense nuclear matter in the form of hyperons, baryons with at least one valence strange quark. Hyperons can form bound nuclear systems with nucleons and create short-lived hypernuclei which decay on the time-scale of the weak interaction (typically ∼200 ps). Our knowledge of nuclear matter is so far restricted to precision nucleon scattering and nuclear data while a generalisation to other quark flavours lacks precision data.  HYPER pioneers a new production mechanism of hypernuclei from antimatter. HYPER will explore the terra incognita of the strange nuclear landscape with unprecedented capabilities. The measured precision ground-state properties and spectroscopy of single-lambda hypernuclei along isotopic chains, from neutron deficient to neutron rich, will give access to the isospin properties of the many-body interactions involving lambda hyperons, and thus to the role of strangeness in the nuclear equation of state and in neutron stars.  The core of HYPER is the development of a new multi-purpose detector with high sensitivity, energy and timing resolutions to be used at the Antimatter Factory of CERN. HYPER has the potential to seed a hypernuclei factory in Europe.
Projet ERC : NEITALG (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : Université technique de Munich — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2086498.0 €

Iterative algorithms (IA) stand as the very backbone of scientific computing. Traditional IA rely on convex optimization to guide the search for optima, severely limiting their scope to local solutions. Consequently, nonconvex problems are regarded as the ultimate challenge of global optimization. Solving them with convergence guarantees will lead to numerous scientific discoveries, such as finding new medications through optimization of the properties of molecules or enhancing materials for solar cells to accelerate advances in green technologies. The first goal of NEITALG is to design new, efficient, and scalable iterative algorithms that can provably solve relevant nonsmooth nonconvex optimizations. This will allow us to overcome locality barriers and open up an entirely new field in scientific computing. Furthermore, parametric IA, which are tailored to training data —making them learnable IA— are currently driving remarkable advances in computing and machine learning. However, most of these systems lack rigorous guarantees of performance or interpretability of results. The second goal of NEITALG is to formulate new learnable IA with focus on interpretability and generalization guarantees. This will greatly advance the development of safer and more reliable technologies based on machine learning. The key approach to achieve both goals involves the systematic study of the largely unexplored mathematical field connecting iterative algorithms and nonlinear evolutions (NE). NEITALG will 1. Explore the principles for which the analysis of NE provides insights into the convergence of an IA; 2. Generate new IA from nonstandard NE to overcome traditional limitations (e.g., locality, lack of interpretability); 3. Provide mean-field optimal control theories for new learnable IA; 4. Obtain guarantees for learnable IA on metric spaces; 5. Develop code and conduct numerical experiments for novel solvers for nonlinear ODE/PDE and new methods for 3D computer vision.

Mots-clés : iterative algorithms,nonlinear evolutions,global optimization,optimal transport,mean field optimal control,scientific computing,machine learning
Projet ERC : CALIDE (Horizon Europe, appel 2023, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Université technique de Munich — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2447299.0 €

Molecules are chiral if they exist in two forms, called enantiomers, which behave like image and mirror image. In many applications, e.g. in medicine, nutrition, and crop protection, chiral molecules are required as a single enantiomer (enantiomerically pure compound) but not as a mixture (racemate). The market for enantiomerically pure compounds exceeds 100 billion € annually and continues to grow rapidly. Despite significant recent advances in asymmetric catalysis, many chiral compounds are for cost reasons prepared as racemates and separated thereafter. If only one enantiomer is required, the other enantiomer remains as waste. The aim of the CALIDE project is to provide for the first time a comprehensive set of methods for the quantitative conversion of racemates into single enantiomers employing exclusively light and a photocatalyst. The process includes the light-induced formation of short-lived achiral intermediates which decay to the starting materials on a thermal reaction channel. By employing two intrinsically different channels for formation and decay, the transformation overcomes issues of microscopic reversibility which render related thermal deracemization reactions impossible. The photocatalyst interacts with the racemic substrates and enables the destruction and reformation of the stereogenic element. A first objective (work package I) is to identify suitable binding motifs to achieve the required proximity in the energy transfer step for compounds displaying an axis or plane of chirality. The second objective (work package II) aims at a deracemization of compounds with a stereogenic carbon center via a reversible cleavage of carbon-carbon or carbon-heteroatom bonds. The bond cleavage can be initiated by energy or electron transfer. The third objective (work package III) tackles substrates with a carbon-hydrogen bond at the stereogenic carbon atom. Bond fission by hydrogen abstraction from the catalyst is proposed as the key feature of this method.

Mots-clés : enantiomerically pure compounds,enantioselectivity,deracemization
Projet ERC : RaRiE (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Université de Leyde — Pays : Pays-Bas — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 670580.0 €

Until recently, the study of penal systems has focused mainly on their most severe or repressive features, but these systems often do great harm when aiming to do good, not least by expanding the scale, reach and intensity of penal control. For example, recent research suggests that ‘rehabilitation’ has become focused on managing risks rather than enabling reintegration and that, in consequence, it often hurts and harms those that it claims to help. By examining evidence from three countries that are often considered ‘progressive’ (the Netherlands, Norway and Scotland), the RaRiE project will help us better understand whether and where rehabilitation lives up to its ideals, and to creatively, critically and comparatively interrogate its development and prospects, its coherences and contradictions, its rhetoric and its realities, its pitfalls and its possibilities. Through a new approach that I call Dialogical Comparative Penology (DCP), building on the interdisciplinary approaches that I have used in a series of innovative, high-impact research projects, RaRiE will provide a uniquely comprehensive analysis of the nature and impact of rehabilitation in these three nations. It will also develop new tools and metrics for critically assessing rehabilitative systems and practices, to better direct their future development. In and through dialogue with policymakers, senior leaders in prison and probation systems, practitioners, activists and people with lived experience of rehabilitation, RaRiE will help to improve the fairness and effectiveness of European penal systems. RaRiE’s ambition -- the ‘step-change’ it offers – lies both in developing a new approach to comparative penology, and in using that new approach to reshape how rehabilitation is understood and developed in Europe.

Mots-clés : criminology,penology,criminal justice,corrections,punishment,rehabilitation,reintegration
Projet ERC : MesoComp (Horizon Europe, appel 2021, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Université technique d'Ilmenau — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2500000.0 €

Turbulent convection flows in nature display prominent patterns in the mesoscale range whose characteristic length in the horizontal directions exceeds the system scale height. Known as the turbulent superstructure of convection, they are absent on both larger and smaller scales and evolve in ways not yet understood; but they are an essential link in the heat and momentum transport to larger scales, an important driver of intermittent fluid motion at sub-mesoscales, and one major source of uncertainty in the prognosis of climate change and space weather. In MesoComp, I will investigate the formation of superstructures in massively parallel simulations of compressible turbulent convection in horizontally extended domains, aiming for a deeper understanding of their dynamical origin and role in the transport of heat and momentum. I will then use these high-fidelity simulations to build recurrent machine learning models to predict the evolution and statistics of the superstructure and thus quantify the transport fluxes beyond the mesoscale. I will also analyse the impact of the mesoscale structures on the highly intermittent statistics at the small-scale of the flow and reveal the resulting feedback in the form of improved subgrid parametrizations by means of generative machine learning. MesoComp opens additional doors to the application of quantum algorithms in machine learning which significantly improve the statistical sampling and data compression properties compared to their classical counterparts. From a longer-term perspective, my research results in a quantum advantage for the numerical analysis of classical turbulence, which accelerates the parametrizations of mesoscale convection and increases their fidelity. This work will finally lead to more precise predictions of the on-going climate change and global warming. The results will also improve solar activity models and thus solar storm prognoses with impacts on satellite communication and electrical grids.

Mots-clés : fluid turbulence,turbulent convection,compressible turbulence,machine learning,quantum machine learning,recurrent neural networks
Projet ERC : SupraVir (Horizon Europe, appel 2021, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Université libre de Berlin — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2849138.0 €

Background: Viral pandemics pose great risks to current and future human health and global trade. Newly emerging viruses can display a broad variety of shapes, such as spherical or filamentous, and spike proteins have different lengths and densities, as seen in coronaviruses and influenza viruses. Viruses can mutate rapidly under evolutionary pressure, resulting in changes to antigen epitopes and reduced efficacy of drugs and vaccines. These variances between viruses and across mutations present challenges to broad-based anti-infection intervention and vaccination. However, the initial docking of viruses to cell surface receptors via heparan sulfate or polysialic acids are common for a number of viruses, offering an attractive target for wide-reaching intervention.  Aim: The SupraVir project will provide a new concept for multivalent supramolecular assemblies as self-adaptive universal virus blockers. This new type of virus inhibitor can adapt to different virus morphologies and mutations by dynamic self-assembly of its virus binding sites. The inhibitor will use a combination of different receptors and bind a great majority of all known viruses by mimicking generic host cell surface receptors.  Methodology: My approach will use self-assembled surface-active supramolecules that mimic the host cell surface efficiently and dynamically. With this method I will avoid a bulk phase that does not contribute to the activity, thus reducing potential toxicity. At the same time, the amphiphilic building blocks can interfere with the viral envelope or capsid and permanently inactivate the virus. Impact: SupraVir addresses the central question: What might prevention of viral infections look like in 2030? I contend that there is a new option, based on mimicking dynamic cell surface receptors with multivalent supramolecular nanosystems that can self-adapt to inactivate rapidly mutating viruses.

Mots-clés : multivalency,supramolecular polymers,synthetic virus inhibitors