Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : TameThiflam (Horizon Europe, appel 2023, Advanced grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Université de Leipzig — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2499000.0 €

The recent Covid-19 pandemic dramatically illustrated (i) the clinical relevance of thrombo-inflammation (the interplay of coagulation and inflammation) and (ii) our limited understanding of molecular mechanisms of thrombo-inflammation. Thrombo-inflammation is a frequent disease-driving mechanism, impairing out-come of viral or bacterial infection, cardiovascular disease, cancer and other diseases. The lack of mechanistic insights hampered the development of specific therapies. My group recently discovered a new molecular link between coagulation and inflammation that addresses this enormous unmet medical need: the initiator of coagulation, tissue factor (TF, F3, CD142), forms a previously unknown heterodimer with the cytokine receptor IFNAR1 (Interferon-alpha/beta receptor alpha chain) on resting cells. In this heterodimer, TF and IFNAR1 are both inactive. Upon stimulation (e.g. fVII/fVIIa, LPS, polyIC) the heterodimer dissociates, activating TF-mediated coagulation, IFNAR1-mediated inflammation, and cellular metabolism. This provides a novel, yet simple molecular switch simultaneously controlling coagulation, inflammation, and metabolism. This “switch” is expected to provide a new conceptional framework for thrombo-inflammatory diseases. To enable future translation of this novel mechanism, I aim to address the following open questions (i) how is this molecular switch regulated in an organ and context specific fashion (in vitro work; cell-specific inducible TF±IFNAR1 deficient mice)? (ii) what are the consequences of this molecular switch for intracellular signaling, inflammation, mitochondrial metabolism (omics with bioinformatic analyses)? and (iii) what are the molecular structures required for the TF-IFNAR1 interaction (cross-linking, crystallography, phage display, computational modeling)? I expect that answering these questions will lead to new diagnostic and therapeutic approaches revolutionizing the way we detect and treat thrombo-inflammatory diseases.

Mots-clés : thrombo inflammation,coagulation,tissue factor,cd142,f3,inteferon,interferon signaling,sterile inflammation,homeostasis
Projet ERC : BRONEB (Horizon Europe, appel 2023, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed Matter Physics
Entité participante : Université de Constance — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2493593.75 €

The power of thermodynamics comes at the expense of certain assumptions and idealizations. An important premise is the concept of a thermal bath which is in contact with the examined system. Typically, such baths are idealized as an infinite reservoir of heat that remains in equilibrium regardless of whether the system in contact is in equilibrium or not. Such description, however, is only valid when the bath relaxation time is much faster than typical time scales of the system. Otherwise, a driven system excites the bath out of equilibrium and will interact with nonequilibrium fluctuations. Recent experiments using colloidal particles within viscoelastic baths, whose relaxation times are comparable to colloidal timescales, have revealed surprising and only poorly understood novel behaviors which are attributed to the nonequilibrium properties of the bath. Examples include particle oscillations inside moving optical traps, a fastened hopping dynamics across potential barriers, an up to 100-fold enhanced rotational diffusion of drifting particles, but also the presence of about one-million-fold increased Magnus forces. Here, we propose a series of novel experiments aiming to uncover the impact of a non-equilibrated bath on the behavior of externally driven colloidal particles but also to exploit such effect for applications. In those experiments we will investigate how nonequilibrium surroundings affect erasure processes of logical information or modify the design of optimal finite time protocols. In addition, we will construct an unprecedented microscopic engine which – unlike conventional heat engines – is not operated between two heat baths but which is periodically coupled to nonequilibrium baths with different relaxation times. The selection of the examples being pursued within this proposal has been chosen not only to demonstrate the manifold consequences of nonequilibrium baths but also provide possible overlaps beyond the field of soft condensed matter.

Mots-clés : colloidal particles,nonequilibrium systems,nonequilibrium baths,non markovian
Projet ERC : ECHo-LE (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : Université de Heidelberg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1814131.0 €

The discovery of the neutrino mass scale is a milestone for shaping the extension of the Standard Model of Particle Physics. Experiments looking at the endpoint region of low energy beta decay and electron capture spectra benefit from a clear signature of massive neutrinos which only depends on energy and momentum conservation. Along with the well-established use of tritium, as in the KATRIN experiment where the present best limit of 0.45 eV at 95% CL was reached, two collaborations, ECHo and HOLMES have pioneered the use of Ho-163. In both experiments, low temperature microcalorimeters enclosing the Ho-163 source are used. Both collaborations have demonstrated the suitability of Ho-163 for a large experiment. The project ECHo-LE (ECHo-Large Experiment) will be built on the hardware and on solid results obtained with the ECHo experiment and will feature large arrays of metallic magnetic calorimeters where each detector encloses Ho-163 for an activity of 10 Bq. A new cryogenic multiplexer which ensures a homogeneous performance will be used for the readout. On-line data reduction and will be developed to allow for a limited saving space. Following the data analysis developed for the ECHo experiment, new fitting procedures will be tested to reduce the effects of systematic uncertainties. The experiment, consisting of 20000 detectors with gaussian detector response having full width at half maximum of less than 5 eV, will be built in the first two years of the project. After installation, the experiment will run for at least two years. The goal is to acquire more than 10 billion Ho-163 events. The analysis of the data will allow for significantly improving the knowledge of the Ho-163 spectral shape parameters and clearly search for an effect of a finite neutrino mass. ECHo-LE will be the first Ho-163 based experiment able to reach sub-eV/c2 sensitivity on the effective electron neutrino mass and set the basis for the ultimate Ho-163 based experiment.

Mots-clés : neutrino mass,ho 163,low temperature microcalorimeters
Projet ERC : SymTrust (Horizon Europe, appel 2022, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université de la Ruhr à Bochum — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2485000.0 €

Symmetric cryptographic primitives protect a large percentage of today's data, whether in transit or storage. This trend is going to increase even more in the future. Consequently, new domain-specific ciphers are needed with significant performance benefits for areas such as memory-encryption, multi-party computation, or crypto-currencies. Great progress has been made regarding their performance. Less progress has been made for security arguments that seek to proof their resilience against malignant attacks. Indeed, most of the security analysis we apply for symmetric ciphers is based on false assumptions and most attacks trying to break those ciphers are flawed. As a consequence, it takes several years to build the necessary trust in the security of any new symmetric cryptographic primitives to be deployed and used. In times of fast innovation cycles where emerging applications are in constant need for new tailored symmetric cryptographic solutions those slow design cycles of symmetric cryptographic primitives are prohibitive and have to be shortened. Within the project, I plan to enable stronger security arguments and tool-based bounds on key-recovery approaches which will accelerate the design process. This will open the path to a new design strategy of trustworthy by design ciphers and contribute to ready-to-use and highly-secure symmetric cryptography suitable to satisfy the needs of future applications. To this end, I will • analyze fundamental properties of symmetric cryptographic primitives especially related to their representation, • derive modular security arguments, • develop a software-assisted approach to optimize and verify the key-recovery part of attacks, • design an efficient cache-encryption algorithm to exemplify the potential of the new design framework. If successful, SymTrust will reshape the whole process of designing symmetric cryptographic ciphers to produce trustworthy by design primitives with the aim to shorten the time-to-deploy.

Mots-clés : symmetric cryptography,block ciphers,security analysis,key recovery attacks
Projet ERC : VCOMP (Horizon Europe, appel 2021, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Université de Maastricht — Pays : Pays-Bas — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 75000.0 €

The distinct advantages of enhancing the public’s voluntary compliance (VC) with regulations has made it an advantageous form of governance. However, its use is limited by the extent to which governments and regulators can trust the public without jeopardizing regulatory purposes and harming other social values. Identifying and analysing the antecedents of VC across doctrines and countries can enhance our theoretical understanding of the underlying nature of the interaction between countries and their residents, as well as evaluate the relative efficacy of behaviourally based regulatory tools. This project proposes, examines, and develops a new conceptual model and a methodology that will facilitate a systematic comparison of the relative efficacy across different doctrines and nations. This model will take into account national, organizational, situational, and individual factors and will draw on and combine material from the fields of organizational, situational, and individual factors and will draw on and combine material from the fields of Compliance, Regulation, Behavioural Public Policy, Behavioural Ethics, Trust & Social Norms on how to advance public VC. We will empirically explore if and to what extent VC and greater trustworthiness by the public can be achieved across countries (high vs. low trust) using regulatory tools such as nudges, pledges, incentives, sanctions, and morality in the context of the situations of tax, environment, COVID-19 and ethics. The comprehensive picture of VC that will emerge from this project will include not just effect sizes, but also factors such as the proportion of those who comply, the sustainability of compliance, the impact on social norms, and the likelihood of positive externalities (e.g., trust enhancement) following the enactment of a specific regulatory tool. Better insights into VC can help elucidate the descriptive and normative understanding of the nature of the interaction between countries and their residents.

Mots-clés : compliance,regulatory instrument choice,behavioral ethics,legal impact,cross cultural legal analysis
Projet ERC : Artificial User (Horizon Europe, appel 2023, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université Aalto — Pays : Finlande — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2499208.0 €

PROBLEM: Despite decades of research on human-computer interaction (HCI), the problem of how to predict human performance with a given user interface (UI) is unsolved. Existing computational models are limited in scope or require large datasets or expert input. Instead, empirical methods are used that are costly and error-prone. OBJECTIVE: This project establishes the foundations of simulation intelligence in HCI through computational rationality. Given a design and a task environment, a simulator generates human-like moment-to-moment behavior autonomously, from which key metrics can be computed, i.e. for learning, performance, and ergonomics. ”Artificial users” can be commanded using natural language without modeling expertise. APPROACH: We seek a breakthrough through the theory of computational rationality that would dramatically expand the models’ scope and actionability. The theory posits that interactive behavior is an emergent consequence of a control policy adapted to internal bounds (cognition) and rewards. While previous work has demonstrated progress, scope has been limited to simple sensorimotor tasks and required reward engineering. This project will study the principles of computationally rational agents that learn skills like humans, can operate autonomously and yet be commanded via natural language. We design a workflow for building dramatically larger models via self-supervised pretraining. IMPACT: We develop a strong complement to existing evaluation methods in HCI. The pushes forward generative modeling in HCI by combining theory-driven causal assumptions about people and modern ML, while being deployed directly in high-fidelity simulators. This allows dealing with novel task environments with higher accuracy. This will be a leap forward in applications of design and engineering (rapid evaluation) and ML (realistic synthetic data) in HCI.

Mots-clés : human computer interaction,simulation models,user modelling,user interfaces,cognitive modeling,evaluation methods,generative models,computational rationality,reinforcement learning
Projet ERC : GETREAL (Horizon Europe, appel 2023, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed Matter Physics
Entité participante : Université Aalto — Pays : Finlande — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2498623.0 €

Conventional materials hosting exotic quantum phases typically have complex atomic structures, inhomogeneities from defects, impurities, and dopants making it difficult to rationally engineer their electronic properties. This can be overcome using van der Waals (vdW) heterostructures that allow an almost arbitrary selection of the heterostructure building blocks, including metals and insulators, charge-density wave systems, superconductors, magnets, correlated insulators, and ferroelectrics. In a vdW heterostructure, the layers interact only through vdW forces and can keep their intrinsic properties. However, proximity effects cause properties to “leak” between the adjacent layers and allow creating exotic quantum mechanical phases that arise from the interactions between the layers. These key features have recently made it possible to realize exotic quantum phases by design and engineer responses that do not occur in natural materials. I will now exploit these features and fabricate heterostructures using molecular-beam epitaxy (MBE) to target artificial heavy fermion heterostructures realizing unconventional superconductivity, artificial 2D multiferroic materials, and 2D quantum spin liquids. The atomic scale geometry and electronic properties of the resulting heterostructures will be characterized using low-temperature scanning tunnelling microscopy (STM) and spectroscopy (STS).  These designer heterostructures will have engineered electronic phenomena with atomically precise structures and controlled interactions. This will lead to exciting new opportunities in fundamental condensed matter physics and subsequently, in quantum devices realizing completely new functionalities. They answer the pressing need for novel quantum materials with tuneable properties to enable completely new types of approaches in quantum technologies. This will keep Europe at the forefront of the second quantum revolution and create yet unimagined future breakthrough technologies.

Mots-clés : van der waals heterostructures,scanning tunneling microscopy stm,scanning tunneling spectroscopy sts,molecular beam epitaxy mbe,multiferroic heterostructures,quantum spin liquids,artificial heavy fermion heterostructures
Projet ERC : ODD-ML (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université Aalto — Pays : Finlande — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2495282.0 €

ODD-ML addresses the open secret of machine learning (ML), which is that model deployment often fails. The problem arises because deployment contexts may differ from the data used to train ML models in unexpected ways. In an increasingly data-driven era, this severely impedes progress in ML-powered R&D and our ability to tackle societal grand challenges with existing ML tools.  To solve this pervasive issue, I propose a radical alternative to current ML approaches, placing human experts at the core of iterative design-build-test-learn (DBTL) loops. My approach comprises the interlinked steps of re-conceptualizing the deployment issue as a need for active learning from domain experts and other indirect sources and, to succeed here, recognizing the imperfect and often tacit knowledge and limited time of human experts, designing ML systems that can rapidly reverse-engineer expert knowledge.  I will achieve this with a combination of ideas transformative for human-AI collaboration: human inductive biases will be inferred from computational-rationality-based cognitive models, amortized on pre-computed solutions for speed, allowing interactive online use. I envision widespread impact in ML, on complex decision-making, and broadly across R&D domains.

Mots-clés : out of distribution generalization,machine learning,active learning,human in the loop learning,distribution shift,probabilistic modelling,computational rationality,sequential experimental design,collaborative ai,decision support
Projet ERC : FutureBAT (Horizon Europe, appel 2021, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Université Friedrich-Schiller d'Iéna — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2499355.0 €

The efficient storage of electric energy represents a major challenge for a successful energy transition, enabling the utilization of fluctuating renewable resources also as base load. Redox-flow-batteries (RFBs) are the only type of battery where intrinsically power and capacity can be varied independently from each other, making this type of battery perfectly suited for scalable stationary applications. RFBs based on aqueous electrolytes with organic / polymer active materials have the potential to be suitable alternatives for commercial metal-based RFBs, with low CO2 footprint perfectly fitting to the goals of the EU Green Deal. In particular, polymer-based RFB systems enable the use of cost-efficient dialysis membranes together with pH neutral table salt solutions as electrolytes. Nevertheless, systems still reveal restrictions in terms of capacity, lifetime and temperature-stability. FutureBAT targets a breakthrough in the development of novel organic active materials for RFBs, by combining the search for new active entities with the improvement of current polymeric materials on the molecular level, by this providing new functions / properties. The key question will be how far polymeric electrolytes can be tuned by adjusting the molecular structure. Advanced polymer structures (incl. (hyper-) branched structures) and colloidal systems (with varied morphologies) as well as novel hybrid organic systems will provide access to hitherto unknown properties, e.g., new photo-rechargeable RFBs or RFBs having all charged species within one single tank. Furthermore, new sensor systems (SOC and SOH) will be applied, which also will form the basis for novel 3D-printed lab cells for (high-throughput) screening. As the outcome, pioneering breakthroughs in the field of polymer-based RFBs will be enabled, surely targeting high risk / high gain step-changing research but built up on the know-how of one of the leading international research teams in this rather new field.

Mots-clés : redox flow batteries,redox active polymers,energy storage
Projet ERC : BindingShadows (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Life Science
Panel : Integrative B iology : from Genes and Genomes to S ystems
Entité participante : Université Friedrich-Schiller d'Iéna — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 3010356.0 €

Bioactivity and ligand binding remain central topics for understanding protein function, for drug discovery and toxicology. Mass spectrometry (MS) allows to detect thousands of small molecules in a biological sample with a single experimental run. Compound annotation is carried out using tandem MS. I will develop machine learning models that predict whether a query small molecule has a certain bioactivity or is binding to a certain protein, where the only information we have about the query molecule is its tandem mass spectra. The identity and molecular structure of the query small molecule is unknown; it is unknown to the models, to me, and potentially even to mankind. Clearly, bioactivity and binding are two flavors of the same problem, and the differentiation is solely driven by the available training data. Notably, elucidating the structure of the small molecule is not the focus of the project, and may be postponed until after experimental confirmation. Models will not predict one particular bioactivity or protein binding, but rather tens in parallel. Evolution has, through variation and selection, optimized the structure of small molecules for tasks such as communication and warfare, and the pool of natural products is enriched with bioactive compounds. My project will allow to harvest the information.  I will apply my models at a repository scale, screening millions of tandem MS spectra in thousands of datasets for small molecules that are most likely to have bioactivity or to bind. I will also make my methods available through our well-established SIRIUS platform, allowing users to derive information about bioactivity without the need of additional experiments. I will enable the worldwide community to hunt for drug leads or unknown toxic compounds, without the need of additional experiments or computational analyses. Finally, being able to find small molecules that bind to a protein can provide helpful information to understand protein function.

Mots-clés : machine learning models,mass spectrometry,metabolomics,small molecules,natural products,discovery,bioactivity,protein ligand binding