Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : CCOO (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université d'Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499828.0 €

Online decision-making, characterized by the need to make decisions without knowledge of the future, lies at the heart of numerous applications. Despite its prevalence, our grasp of effective strategies for handling the associated uncertainty remains poor. Through the lens of the established framework of online algorithms as well as the emerging field of learning-augmented algorithms, this project aims to address central challenges in decision making under uncertainty.  While there has been extensive research on online algorithms, many of the field's core challenges remain unresolved. However, several recent discoveries of new algorithmic design and analysis techniques have opened up novel avenues for overcoming previous obstacles.  Alongside these technical advancements, the rise of machine learning is now significantly enriching our toolset for dealing with uncertainty. This has motivated the recent emergence of the field of learning-augmented algorithms. Here, an algorithm's input is augmented with predictions, aiming for near-optimal performance if predictions are reasonably good, while still retaining classical worst-case guarantees even for highly erroneous predictions.  Inspired by these recent developments, this project aims to substantially elevate our understanding of decision-making under uncertainty. The main objectives are (1) to explore new directions around the concept of work functions, (2) to elevate the mirror descent technique into a generic tool for online algorithm design, (3) to develop universal techniques for designing learning-augmented algorithms, and (4) to expand the scope of learning-augmented algorithms to new domains.  The project addresses questions at the forefront of theoretical computer science, building on the PI's recent success in resolving several long-standing problems, and strives for foundational contributions to the timely issue of leveraging machine-learned predictions for improved algorithm design.

Mots-clés : online algorithms,learning augmented algorithms
Projet ERC : GLYCvsPROT (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: B iological  Mechanisms , Structures  and Functions
Entité participante : Université d'Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1492478.0 €

To enter a host cell a virus needs to bind a receptor on the cell surface. Typically, viruses use specialised glycoproteins to bind to either glycans or proteins as receptors. The specificity and affinity of a virus glycoprotein for cellular receptors is a key determinant for which hosts a virus can infect. Certain enveloped viruses of high pandemic potential, such as influenza A and coronaviruses, have an ability to change their receptor specificity from a glycan to a protein receptor, and in some instances, even bind both with one glycoprotein. These receptor transitions might have contributed to emergence of pandemic viruses, such as SARS-CoV-2, and may underlie future pandemics.  Studying the structure and evolution of viral glycoproteins and their receptor complexes can help to understand the mechanisms viruses use to cross the human species barrier. I will build on my recent studies and pilot data on the structure and evolution of viral glycoproteins, and employ the glycoproteins of influenza viruses and coronaviruses which can bind to either or both receptor types to show:  - how different receptor types are recognised structurally by very similar viral glycoproteins - how receptor type affects interaction avidity and mechanisms of viral fusion - how receptor transitions may arise during viral evolution and influence its direction  This will be achieved by combining structural, biochemical, and biophysical approaches to reveal interactions between viral glycoproteins, influenza haemagglutinins and coronavirus spikes, with glycan and protein receptors. My studies will provide key new insights into the plasticity of viral glycoproteins and the evolutionary pathways associated with changes in receptor specificity, thus shedding light onto emergence of pandemic viruses such as SARS-CoV-2 and pandemic influenza A viruses. Importantly, this will also facilitate monitoring of emerging pathogens and increase our future pandemic preparedness.

Mots-clés : viral entry,viral receptors,influenza a,coronaviruses,cryoem,crystallography
Projet ERC : NARFB (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Université d'Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1486863.0 €

Humans and other animals learn from experience and generalise it to new situations. This generalisation permits rich inferences from sparse data and building novel plans in the face of new obstacles. Yet our understanding of the neural mechanisms underpinning flexible behaviours is limited to simple situations such as spatial learning and only in the hippocampal formation. Flexibly behaviour and generalisation is much more than just space and the hippocampal formation – it is for any structured knowledge. Flexible generalisation lets you know that presents should be wrapped before being opened, in the same way as knowing that you can only vote if you are already registered. Such structured knowledge, and their corresponding tasks, are often hierarchical, compositional, and require inferences., yet we have little understanding of the neural mechanisms of hierarchical, composition, or inference. This grant aims to build an understanding of the underlying neural algorithms and representations permitting these cognitive functions. By combining modelling and theory, we will investigate the optimal neural representations and algorithms for solving these tasks and relate them to prefrontal cortex and the hippocampal formation. We will use recurrent neural networks trained to learn and generalise on structured tasks. Furthermore, we will understand these systems theoretically - providing a mathematical formalism for optimal neural representations and algorithms that can explain when and why neural systems learn different representations and algorithms. We will collaborate with experimentalists to test model and theory predictions. Lastly, we will use the understanding gained from neuroscience tasks to build bridges between natural and artificial intelligence. Building such an understanding will offer deep insights into the neural mechanisms of cognition, how and when it dysfunctions, and allow us to interpret modern AI systems that have an increasing impact on humanity.
Projet ERC : NetFair (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Université technique de Graz — Pays : Autriche — Rôle : PI
Acronyme de l'entité : TU Graz
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1481736.0 €

Social inequalities are on the rise and will have devastating impacts on education, healthcare, economies and societies for many generations to come. Structural barriers to equality, despite being complex and dynamic, are poorly understood through the lens of complex evolving social networks. More crucially, with the rise of AI and machine-learning algorithms, it is extremely important to detect, forecast, and mitigate those inequalities in a systematic manner in order to avoid unintentional algorithmic consequences.  A network fairness framework is proposed, premised on topological and temporal features of social interactions that shape the formation and evolution of inequalities. These features include (1) people have multiple and correlated attributes that determine how they identify with groups and interact with others, (2) people belong to a variety of social groups with different sizes, hierarchies, and historical precedents, and (3) interactions between people evolve over time in a hybrid space of society and algorithms.  To this end, I will develop a suite of dynamical complex network models of inequality that are driven by social theories (e.g., homophily, intersectionality, consolidation) and calibrated and evaluated with big data and network experiments. This will allow us, for the first time, to investigate inequalities that arise from network-based algorithms in a systematic manner. More importantly, I will devise a novel methodology of network intervention, a set of data-driven principles for tackling network inequalities in a broad range of applications. Finally, based on the models developed in this project, I will create a cutting-edge interactive software – NetFair – to visualize and forecast the evolution of inequalities and implement various fairness criteria. The software will contribute towards bridging the gap between research and policy applications in academia and industry.

Mots-clés : complex networks,computational social science,intersectional inequalities
Projet ERC : SPES (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université d'Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1494803.0 €

Satisfiability modulo theories (SMT) is a ubiquitous framework in modern software verification. It can express a significant number of classical program verification tasks, e.g. invariant generation, reachability and pre/post-conditions, in complex programs with arrays, pointers, real/integer variables, and user-defined data structures. State-of-the-art SMT solvers, such as cvc5/Z3, form the backbone of modern verification suites.  Unfortunately, SMT-based verification methods are far from perfect in terms of scalability. They are especially slow when the desired property depends on a large portion of the program, such as a function with hundreds/thousands of lines. A primary reason is that solvers are designed for *general-purpose* constraint-solving. When a program analysis tool, such as Boogie, reduces a verification task to SMT and passes it to a solver, such as Z3, the solver has no knowledge of the original program or any of its structural and sparsity properties that are inherited by the SMT instance. Thus, it spends significant time exploring unpromising solution paths or applying unproductive heuristics.  In this project, we will exploit the structural sparsity of programs to design faster bespoke SMT-solving algorithms for software verification tasks. It is well-known that control-flow graphs of structured programs are sparse and decomposable. Formally, they have bounded treewidth/pathwidth. Thus, one can successively find small cuts that decompose the graphs into parts of a small size. The idea is to guide the SMT-solver with a promising approximation or decision order based on these decompositions. This is because the SMT instance inherits the sparsity and structure of the underlying program. Thus, it can be decomposed into small parts by successively solving low-dimensional sub-instances. More specifically, this project will use the decomposition to design faster algorithms for satisfiability checking and (approximate) quantifier/variable elimination.

Mots-clés : program verification,graph theory,graph sparsity parameters,automated reasoning
Projet ERC : LeGo (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Université de Berne — Pays : Suisse — Rôle : PI
Acronyme de l'entité : Unibe
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499875.0 €

Since 2018, the European climate movement has embraced civil disobedience to denounce government inaction on climate mitigation. Governments increasingly respond with criminalization of climate civil disobedience, while NGOs, activists and transnational institutions (e.g. Amnesty Intl., the UN) defend it. Criminalization and defense of civil disobedience highlight a growing tension between state efforts to restrict political protests (often by treating protests as a matter of security), and transnational efforts to legitimize protests (usually treating them as a matter of rights). LeGo advances our understanding of criminalization and rights-based defense of climate civil disobedience in the UK (common law system) and in Germany (civil law system). Both countries are at the center of a growing repression of climate civil disobedience. To date, there is little understanding of how criminalization and defense of climate civil disobedience affect activists and the climate movement. The main research objective is to examine how the use of criminal law and human rights discourses shapes the legal consciousness and conduct of climate activists. To this end, the project examines criminal trials against climate activists; state efforts to pass new laws and mobilize criminal codes and security frames to prosecute activists; efforts of transnational actors and institutions to counter criminalization; and climate activists’ perceptions of and responses to criminalization and rights-based defense. LeGo adopts a mixed method approach drawing on methods and concepts from political and legal anthropology and geography, social movements and mobilization studies, and critical legal studies. Theoretically, the project advances our understanding of how environmental activism is governed through the uses of law and rights. Empirically, the project highlights the consequences of criminalization on the European climate movement, (trans)national climate politics, and the rule of law.

Mots-clés : governmentality,civil disobedience,climate activism,law,human rights
Projet ERC : BAC-MUNITY (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems E ngineering
Entité participante : Université de Berne — Pays : Suisse — Rôle : PI
Acronyme de l'entité : Unibe
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2499744.0 €

In times of rising antimicrobial resistance (AMR), natural predators of bacteria have great potential as alternatives. However, as with antibiotics, their efficacy is modulated by the ability of bacterial targets to develop resistance. For bacterial predators this relates specifically to understanding predator-prey interactions and particularly prey defence to attack and early stages of infection. However, in stark contrast to phage defence systems, little is known about bacterial prey resistance to predatory bacteria, and barely anything about prey bacterial immunity against predatory bacteria which is the last line of prey defence in the periplasm or cytosol.  While the factors determining bacterial immunity against predatory bacteria are unknown, recently I have detected, for the first time, novel evolutionary hotspots in the bacterial prey genome in response to predatory pressure. This reveals unexplored opportunities to elucidate mechanistically bacterial immunity against predatory bacteria. The goal of BAC-MUNITY is to define the systems generating bacterial immunity against predatory bacteria. Appling methods from genetic engineering, protein interaction and various microscopy techniques (time-lapse widefield, super-resolution), we will (i) identify the mechanisms of bacterial prey immune systems against predatory bacteria, and (ii) determine specificity in relation to different predatory bacteria and elucidate how universal they are amongst different bacterial prey. Further, we will (iii) investigate regulation using predators engineered to overcome prey immunity and prey with genetically inactivated self-protection.  Unravelling new bacterial immune systems against predatory bacteria will open new research avenues with great potential in biotechnology, while providing valuable knowledge on the predator-prey arms race. It will contribute to the development of novel, sustainable treatments against AMR by exploiting the potential of predatory bacteria.

Mots-clés : microbial interactions predator prey interaction predatory bacteria bacterial immunity genetic engineering gram negative bacteria molecular mechanisms antimicrobial therapy
Projet ERC : BGCTip (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Université de Berne — Pays : Suisse — Rôle : PI
Acronyme de l'entité : Unibe
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1498700.0 €

CO2 emissions are the primary driver of anthropogenic climate change, and it is thus vital to enhance our understanding of the global carbon cycle, its changes, internal processes, and feedbacks with Earth’s climate system. Climate change further threatens to push the Earth system across multiple tipping points, which could have significant, but yet unconstrained impacts on the carbon cycle. BGCTip will address these challenges by investigating the potentially cascading interactions of climate and ocean tipping points with marine biogeochemical systems that drive CO2-uptake. By leveraging the extensive information of Earth’s past abrupt climatic events of the last glacial cycle, BGCTip aims to decipher the triggers and feedback mechanisms that govern the complex relationship between climate and the carbon cycle. This will be achieved through a novel combination of methods that link proxy reconstructions with biogeochemical isotope modeling. An intermediate complexity Earth system model will be used and further developed capable of transiently simulating these past climate transitions. New isotope proxies (δ15N, δ30Si, δ138Ba, and δ60Ni), that trace various aspects of marine biogeochemistry, will be implemented in the model. For the first time, these new biogeochemical proxies will be employed together with ocean circulation proxies in a coherent model framework constrained by global compilations of proxy reconstructions. This will provide a comprehensive understanding of the impacts of past climate tipping on the biological pump and oceanic carbon uptake. Exploiting these paleo-constraints, the impact of tipping points will then be systematically assessed across the full range of available future climate scenarios.

Mots-clés : paleoceanography future climate scenarios marine biogeochemistry climate tipping points model development
Projet ERC : Calcifer (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe Sciences
Entité participante : Institute of Science and Technology Austria — Pays : Autriche — Rôle : PI
Acronyme de l'entité : ISTA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499309.0 €

While stellar evolution has been considered one of the most well-understood astrophysical processes for decades, a massive revolution is in motion thanks to probes of dynamical processes in stellar interiors with asteroseismology. Through this study of stellar oscillations, core rotation-rate measurements point out a very efficient extraction of angular momentum in the depths of stars. It represents one of the most fundamental, yet poorly understood, processes throughout stellar evolutionary journeys. This critical knowledge gap casts significant doubt on our ability to accurately date astronomical objects in the Universe using current stellar models, as an efficient angular momentum transport would bring fuel to the burning core and therefore extend the life of the star. Intermediate-mass stars (IMStars) are the perfect laboratory to elevate our understanding of angular momentum transport in stars, as their structure and internal dynamics resemble that of low-mass stars in the late stages of evolution, as well as high-mass stars during the earliest stages. The recent discovery of surprising oscillation mode frequencies and amplitudes revealed the presence of stable magnetic fields in the radiative core of a few red giant stars, similar to fields observed at the surface of 10% of IMStars during the preceding phases and later on when they lose their envelope. The Calcifer ERC project aims at pioneering magnetoasteroseismology, through an unprecedented exploration of the evolution of magnetic IMStars. We will use observed stellar oscillations to unlock the invisible, unveiling magnetic processes controlling the dynamics of the plasma in the depths of IMStars along their evolutionary journey. The ultimate objective of this ambitious and unifying project is to deliver the first meticulously informed model of the magnetized evolution of IMStars from birth to death, fundamentally impacting our ability to date structures in the Universe.

Mots-clés : stellar evolution stellar magnetism stellar rotation stellar ages asteroseismology
Projet ERC : DNA-TO PAss (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: B iological  Mechanisms , Structures  and Functions
Entité participante : Institute of Science and Technology Austria — Pays : Autriche — Rôle : PI
Acronyme de l'entité : ISTA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499711.0 €

Here I propose to create a new class of designed nanomaterials that will combine the advantageous features of protein design and DNA nanotechnology: nucleic acid-templated protein assemblies. I propose three different approaches that all utilize the addressability of nucleic acids on the nanometer to micrometer length scale to control size, shape, and composition of designed protein assemblies. In the first approach, the structural and mechanical properties of the assembly will be defined by the protein components, while the nucleic acid component serves merely to define the dimensions of the assembly and to introduce addressability to an otherwise symmetric, repetitive assembly. All components, including the nucleic acid template, can be genetically encoded, potentially enabling assembly of entire nanoparticles inside living cells. The second approach uses more complex nucleic acid templates, such as DNA or RNA nanostructures, to control size, shape, and addressability of two- or three-dimensional protein assemblies. The shape of the final protein assembly reflects the shape of the templating nucleic acid nanostructure, and the protein assembly can be viewed as a coating that adds rigidity, stability, and, crucially, biological functionality to the template nanostructure. Both approaches one and two are amenable to library-scale screening by coupling size and shape of the particles as well as patterning of functional domains (“phenotype”) to the sequence of the nucleic acid template (“genotype”). In a third approach, the nucleic acid is not incorporated into the final assembly, but merely serves as a “mold” to define size and composition of a protein assembly. A single DNA origami mold could thus “catalyze” the assembly of many nanoparticles, circumventing potential scalability bottlenecks from approach two. These assemblies use the synergy between DNA nanotechnology and protein design to achieve properties that would not be accessible to either technology alone.

Mots-clés : dna nanotechnology,dna origami,protein design,molecular design,bionanotechnology,dna binding proteins