Data-Gouv-ML/pcri-actions-marie-curie-participations-et-projets-laureats huggingface dataset:
Projet miRAQEL (Horizon Europe, appel à projets 2023)
Action : Postdoctoral Fellowships - Global Fellowships
Panel : Life Sciences
Statut du projet : SIGNED

MicroRNAs (miRNAs) are a class of highly conserved small non-coding RNAs that regulate the expression of most protein coding genes. MiRNAs have been extensively studied, not only to understand their molecular function but also due to their potential as therapeutic and diagnostic tools. Most current studies rely on small RNA sequencing (sRNA-seq), recently extended to single cells, because it allows to measure all miRNAs present in a sample even at low input amounts. However, state-of-the-art statistical analysis of sRNA-seq data is still performed using standard mRNA-seq software. This is inappropriate because miRNA counts violate several assumptions of these methods. In extreme cases, upregulated miRNAs will falsely seem down-/upregulated because of technical effects. Technical bias is even more severe in low input applications, e.g., single-cell, so decreasing the RNA input lowers quantification accuracy. By developing statistical methods specifically tailored to model sRNA-seq complexities, miRAQEL aims to dramatically improve the accuracy of miRNA quantification through sRNA-seq, contributing to solve challenges in the field. Appropriate statistical methodology is urgently needed to fully elucidate the role miRNAs play in many diseases and improve their biomarker potential.  The fellow will receive specialized training in standard single-cell RNA-seq and statistical analysis of miRNA data from a leading research team in the field. In turn, he will bring his pipeline development and miRNA genomics expertise to implement and evaluate sRNA-seq modelling software using ground truth data and simulations. A placement will ensure transfer of this knowledge to an European start-up. The emerging unique skill set will provide him with a privileged interdisciplinary view that he can leverage to kick-start an independent academic career in the analysis of small RNA molecules at the single cell level, getting ahead in a field that is expected to develop in the coming years.

Mots clés : microrna isomirs statistical modelling small rnas small rna sequencing srna seq benchmarking data normalisation differential expression analysis single cell small rna seq

Participants :
- Université de Rochester (Etats-Unis) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
- Université de Grenade (Espagne) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 315332.8 €
- Destina genomica slne (Espagne) — Partner, associated partner, Organismes privés (hors enseignement), subvention : 0.0 €
- Université Johns-Hopkins (Etats-Unis) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
Projet AstroError (Horizon Europe, appel à projets 2024)
Action : Postdoctoral Fellowships - Global Fellowships
Panel : Life Sciences
Statut du projet : SIGNED

Updating behavior based on environmental feedback is essential for organisms to learn and develop optimal behavioral strategies. Norepinephrine (NE) release from the locus coeruleus (LC) has been recently shown to critically mediate reinforcement learning (RL), such that a high level of NE released after a reward prediction error (RPE) improves subsequent behavior. However, the mechanism by which transient LC-NE neuron activity (lasting 50-300 ms) influences behavior several seconds later remains unclear. Building on experimental findings in the Outgoing Phase supervisor’s laboratory, we hypothesize that astrocytes play a previously unreported role in RL: they temporally amplify specific NE-RPE signals, facilitating the exploitation of neuronal sensory information when it is needed to optimize subsequent behavior. We will now probe the theoretical advantages of the novel NE-astrocyte RL mechanism by combining advanced computational analysis and modeling of rich neuronal and astrocytic cortical population recordings during RL. We will investigate computationally and theoretically the role of NE and astrocytes in modulating neuronal population dynamics supporting post-reinforcement behavioral gain. We will use data sets available in the Outgoing Phase supervisor’s laboratory to address three main scientific questions: 1) How do NE-RPE signals affect the future encoding of task-relevant information in neuronal populations? 2) How do in vivo neuro-astrocytic interactions support cross-population information processing critical for RL? 3) What is the role of NE-astrocytic signaling in RL - how is it biophysically supported, and implemented in novel RL algorithms and recurrent astrocyte-neuron networks (RANNs)? Altogether, I expect this proposal to be transformative in advancing our understanding of learning mechanisms in biological systems, and that they will potentially inform new algorithms for RL based not only on networks of neurons but also including astrocytes.

Mots clés : astrocytes neural coding reinforcement learning prediction error information theory norepinephrine purinergic signaling

Participants :
- Universitätsklinikum Hamburg-Eppendorf (Allemagne) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 424992.0 €
- Institut de technologie du Massachusetts (Etats-Unis) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
Projet QSDL (Horizon Europe, appel à projets 2024)
Action : Postdoctoral Fellowships - Global Fellowships
Panel : Information Science and Engineering
Statut du projet : SIGNED

Deep Neural Networks (DNNs) have sparked a revolution across multiple domains. However, the escalating computational demands of advanced DNNs have led to the need for high-power consumption accelerators, which hinders their deployment on low-power devices. To enable advanced DNNs on such devices, the industry has resorted to offloading computationally intensive DNN inference to cloud servers. Nevertheless, this offloading architecture introduces vulnerabilities that compromise data security, presenting a pressing challenge, particularly in AI applications where data privacy is essential. In response, I propose a pioneering approach that harnesses the quantum nature of light to establish a practical paradigm for information-theoretically secure deep learning, where clients safeguard their sensitive data, and servers protect their proprietary AI models. This project delves into information science, aiming to explore theoretically and experimentally whether quantum science can offer practical advantages in privacy-preserving inner products against limited adversaries. The innovative essence of this proposal lies in the demonstration of quantum private deep learning in Prof. Englund's lab at MIT, utilizing an optical network. The experimental validation of the protocol with real-life healthcare devices in collaboration with Prof. Katz (HUJI) will unlock a new era of machine learning applications, including the secure utilization of AI in healthcare (e.g., IBM's Watson, Aidoc’s aiOS) and AI assistance (e.g., ChatGPT, Bard). This work may also reshape regulations like the EU General Data Protection Regulation.

Mots clés : quantum cryptography, optical communication, secure computation, deep learning

Participants :
- Université hébraïque de Jérusalem (Israël) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 415823.64 €
- Institut de technologie du Massachusetts (Etats-Unis) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
Projet Econ-ML (Horizon Europe, appel à projets 2021)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Economic Sciences
Statut du projet : CLOSED

Modeling behavioral patterns of commuters and their decision-making process is crucial to develop sustainable and effective transport policies, predict and forecast the travel mode choices of a certain population with respect to changes in some attributes or components of the transportation system, and determine the different sources of heterogeneity in tastes and preferences. Econ-ML is about developing hybrid frameworks that combine several machine learning techniques with econometric discrete choice models to better account for different aspects of unobserved heterogeneity within a population such as systematic and random taste variations in addition to market segmentation. The proposed models would abide by McFadden’s vision of an appropriate econometric choice model in order to maintain the behavioral interpretability while improving the prediction and forecasting capabilities. Moreover, this project will focus on estimating the proposed models using Bayesian Variational Inference (VI) techniques and on providing solutions to overcome the corresponding limitations of such methods. In addition, a comparison of traditional estimation techniques such as Maximum Simulated Likelihood Estimation (MSLE) and Expectation-Maximization (EM) with Bayesian Variational Inference techniques will be conducted, with the aim of providing recommendations on when each estimation method should be used. The ultimate goal is to apply the proposed framework to real-world case studies (e.g., shared mobility, biking behavior in Copenhagen, adoption of electric vehicles, etc.) and provide the authorities and operators with forecasts and recommendations for new policies that might mitigate the negative impacts of the transportation system.

Mots clés : demand modeling econometric models discrete choice models machine learning variational inference heterogeneity

Participants :
- Université technique du Danemark (Danemark) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 214934.4 €
- Institut de technologie du Massachusetts (Etats-Unis) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
Projet MLP4ProSim (Horizon Europe, appel à projets 2023)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Chemistry
Statut du projet : SIGNED

Atomic simulation of the proteins is one of the main tools in understanding the functions, interactions, and dynamics of these vitally important biomolecules and plays a pivotal role in numerous cutting-edge researches such as elucidating the pathways of diseases, drug discovery and so on. The limited accuracy of the classical force fields specially in approximating non-bonded interactions as well as in capturing kinetics of proteins has attracted attentions towards more advanced alternatives. MLP4ProSim aims to develop next-generation transferable all-atom machine-learned potentials (MLPs) for computer simulation of proteins. To assure the applicability of the developed potentials for more realistic and biologically relevant simulations, training of the MLPs is carried out using a critically assessed dataset containing both quantum-mechanically computed and experimentally determined reference data. Enabling transferability of the developed MLPs and their applicability for the simulation of different proteins without requiring demanding retraining processes will be addressed by considering training sets containing proteins with diverse shapes, functions, and sizes. The resulting MLPs, after being validated for reliability and transferability via extensive benchmark tests, will be implemented in LAMMPS as an extensively applied molecular dynamics simulation code to make it openly accessible to the community. The successful completion of the project objectives is expected to have significant impacts in a broad range of scientific and technological areas. In addition to the anticipated outstanding scientific impacts, this project encompasses comprehensive trainings planned for the experienced researcher Dr. Amin Alibakhshi in accordance with the objectives of the Horizon Europe Framework Programme. This training aims to equip him with the essential skills and innovative capabilities required to emerge as a future leader at the forefront of academic excellence.

Mots clés : molecular dynamics, protein simulation, neural network potentials, machine learning, atomistic simulation

Participants :
- École internationale supérieure d'études avancées (Italie) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 188590.08 €
- Université de la Ruhr à Bochum (Allemagne) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
Projet MEMOCAUSE (Horizon Europe, appel à projets 2021)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Social Sciences and Humanities
Statut du projet : SIGNED

On the causal theory of memory, remembering an event requires a causal connection to a past experience. I can remember my last birthday because I have experienced it before. On the simulation theory, in contrast, remembering a past event is very much like imagining a future one: in both cases, a causal connection to a first-hand experience of the event is not necessary. The debate between causalists and simulationists has been at center stage in the thriving new field of philosophy of memory. Despite this, there has been no systematic analysis of the notion of causation relevant to memory. As a result, the nature of memory causation has remained largely unexplored.  This project aims to fill this significant lacuna. It will develop the first comprehensive theory of memory causation, bringing together results from the philosophy of causation and the interdisciplinary study of memory. It will aim to clarify what it means to say that memories are caused by past experiences and to establish whether such causation is necessary. The project's core thesis is that claims about memory causation should be relativized to causal models — mathematical models that represent the functional relationships between variables in a system. A memory is caused by a past experience iff, relative to an appropriate model of the memory system, it is counterfactually dependent on it, but only in precisely defined contexts. If all memories are so dependent, then causation is necessary for remembering. With a proper formulation, the causal necessity thesis can be assessed. The working hypothesis is that the thesis faces substantial difficulties and is likely false. Hence, causal theories fail and the simulationist rejection of the necessity of appropriate causation is warranted. The project will make an innovative and much-needed contribution to the central debate of contemporary philosophy of memory, helping us understand what memory is.

Mots clés : philosophy of memory causal theory of memory simulation theory of memory causation causal models memory traces

Participants :
- Université Grenoble Alpes (France) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 211754.88 €
- Université de la Ruhr à Bochum (Allemagne) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
Projet RAMPART (Horizon Europe, appel à projets 2024)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Life Sciences
Statut du projet : SIGNED

RAMPART aims to address the significant clinical challenge of tumor resistance to anticancer therapies, a key factor contributing to treatment failure and decreased patient survival, particularly in Head and Neck Squamous Cell Carcinoma (HNSCC). The project’s primary objective is to develop a label-free, nondestructive and rapid methodology using Raman and multimodal imaging to identify biomarkers of tumor resistance, by monitoring the biochemical responses of ex vivo patient-derived tumor slices to both immunotherapy and radiotherapy. To do so, RAMPART will employ cutting-edge Raman spectroscopy, including both spontaneous (SR) and a multimodal microscope (SRS-TPEF-SHG), to perform high-resolution biomolecular mapping and imaging of patient-derived tumor samples. This approach allows for precise detection of biomolecular changes within the tumor microenvironment (TME), offering a comprehensive understanding of the factors driving therapy resistance. The project will be conducted at Politecnico di Milano (main host), and two secondments at Jena University Hospital (UKJ) and Johns Hopkins University (JHU), where I will prepare patient-derived HNSCC slices. The relevance of RAMPART lies in its potential to significantly enhance clinical decision-making in HNSCC oncology, advancing the state-of-the-art in tumor evaluation, with label-free and nondestructive approaches for biomolecular tumor imaging. By establishing a protocol for the early detection of resistance biomarkers, the project will enable rapid, personalized treatment strategies tailored to individual patients, driving to decision-making time of 5-7 days. Additionally, the insights gained into tumor heterogeneity and the TME will contribute to a more profound understanding of their roles in treatment outcomes. The project’s findings are expected to potentially impact on the field of precision medicine, ultimately improving survival rates and quality of life for patients with HNSCC.

Mots clés : raman, tumor resistance, therapy simulation, biomolecular imaging, label free

Participants :
- École polytechnique de Milan (Italie) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 209483.28 €
- Jena University Hospital (Allemagne) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
- Université Johns-Hopkins (Etats-Unis) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
Projet INTERACT (Horizon Europe, appel à projets 2021)
Action : Postdoctoral Fellowships - Global Fellowships
Panel : Information Science and Engineering
Statut du projet : SIGNED

Building machines that interact with their world, discover interesting interactions and learn open-ended repertoires of skills is a long-standing goal in AI. This project aims at tackling the limits of current AI systems by building on three families of methods: Bayesian program induction, intrinsically motivated learning and human-machine linguistic interactions. It targets three objectives: 1) building autonomous agents that learn to generate programs to solve problems with occasional human guidance; 2) studying linguistic interactions between humans and machines via web-based experiments (e.g. properties of human guidance, its impact on learning, human subjective evaluations); and 3) scaling the approach to the generation of constructions in Minecraft, guided by real players. The researcher will collaborate with scientific pioneers and experts in the key fields and methods supporting the project. This includes supervisors Joshua Tenenbaum (program synthesis, MIT) and Pierre-Yves Oudeyer (autonomous learning, Inria); diverse collaborators, and an advisory board composed of an entrepreneur and leading scientists in developmental psychology and human-robot interactions. The 3rd objective will be pursued via a secondment with Thomas Wolf (CSO) at HuggingFace, a world-leading company in the open source development of natural language processing methods and their transfer to the industry. By enabling users to participate in the training of artificial agents, the project aims to open research avenues for more interpretable, performant and adaptive AI systems. This will result in scientific (e.g. interactive program synthesis approaches), societal (e.g. democratized AI training) and economic impacts (e.g. adaptive AI assistants). The dissemination, communication and exploitation plans support these objectives by targeting scientific (AI, cognitive science), industrial (video games, smart homes) and larger communities (gamers, software engineers, large public).


Participants :
- Institut national de recherche en sciences et technologies du numérique (France) — Coordinator, beneficiary, Recherche, subvention : 276681.6 €
- Institut de technologie du Massachusetts (Etats-Unis) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
Projet DECONSTRUCT (Horizon Europe, appel à projets 2024)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Chemistry
Statut du projet : SIGNED

Cyclic amines are high-value molecules found in both natural products (alkaloids, proline…) and small-molecule therapeutics. The development of strategies for the selective and divergent functionalization of these derivatives can provide new opportunities for the discovery and evolution of materials of high relevance to our society.  In general, cyclic amines are functionalised by introduction of new functionalities in place of C–H bonds. In this work we propose a fundamentally novel way to modify amines by means of radical deconstruction and tandem functionalization. This strategy will convert these cyclic materials into acyclic ones and further introduce additional substituents.  The chemistry will involve amine activation by simple coordination with BH3. This will enable access to a boryl radical that can undergo e fast fragmentation followed by a radical functionalization process. Overall, this strategy will constitute a divergent platform for the diversification of high-value amines and also a novel retrosynthetic tactic for the preparation of acyclic derivatives. This research squarely fits within the expertise of the Leonori group in the generation and use of boryl radicals in synthesis and catalysis.  The completion of such an innovative and ambitious project at RWTH Aachen University will be facilitated by generating, transferring, sharing and disseminating knowledge, and will enhance the Researcher future career following the training plan envisioned.

Mots clés : boryl radical, radical ring opening, copper catalysis, cross couplings

Participants :
- Université technique de Rhénanie-Westphalie à Aix-la-Chapelle (Allemagne) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 202125.12 €
Projet BRAT (Horizon Europe, appel à projets 2023)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Chemistry
Statut du projet : SIGNED

Aliphatic molecules are integral to every aspect of chemistry and radical chemistry has strong impact in their preparation. However, radical reactions are heavily influenced by the polarity of these species, which ultimately controls their reactivty profile.Most notably, standard alkyl radicals are nucleophilic and therefore do not react with electron rich aromatics and olefins. Electrophilic radicals display strong reactivity with these species but they are of limited synthetic value.  This project seeks to address this challenge by developing an innovative approach for radical "umpolung". In particular, we will demonstrate the integration of photoredox catalysis and boryl radical-mediated H-atom transfer catalysis, as a unique blueprint for the activation of alpha,alpha-diborylated alkalnes. This will generate alpha,alpha-diborylated radicals that, owing to their strong electrophilic character, will (i) react with electron rich aromatics and olefins and then (ii) enable diverge functionalization. In this way we will overcome the fundamental issue that nucleophilic alkyl radicals do not react with electron rich olefins/aromatics. This reactivity will streamline the preparation of many high-value but difficult to make aliphatic products, will be used in late-stage functionalizations and will substantially expand the fields of photoredox catalysis and boryl radical chemistry.  This research capitalizes on recent developments of the Leonori group that has experience in the design of methodologies based on both H-atom transfer and boryl radical reactivity. The completion of such an innovative and ambitious project at RWTH Aachen University will be facilitated by generating, transferring, sharing and disseminating knowledge, and will enhance my future career following the training plan envisioned.

Mots clés : boryl radicals, h atom transfer, radical philicity, radical umpolung, cross couplings

Participants :
- Université technique de Rhénanie-Westphalie à Aix-la-Chapelle (Allemagne) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 189687.36 €