Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Nuclear Reactions for Neutrinoless Double Beta Decay (NURE)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Fundamental constituents of matter
Panel description: Particle, nuclear, plasma, atomic, molecular, gas, and optical physics
Countries involved: Italy,Italy
Call: ERC Starting Grant (ERC-2016-STG, 2016)
Start date: 2017-04-01
End date: 2023-09-30
Duration (months): 78.0
Total project cost (EUR): 1272000.0
EU funding (EUR): 909812.5
Participants: 2
Project status: SIGNED

Neutrinoless double beta decay (0νββ) is considered the best potential resource to determine the absolute neutrino mass scale. Moreover, if observed, it will signal that the total lepton number is not conserved and neutrinos are Majorana particles. Presently, this physics case is one of the most important research “beyond the Standard Model” and might guide the way towards a Grand Unified Theory of fundamental interactions. Since the ββ decay process involves nuclei, its analysis necessarily implies nuclear structure issues. The 0νββ decay rate can be expressed as a product of independent factors: the phase-space factors, the nuclear matrix elements (NME) and a function of the masses of the neutrino species.Thus the knowledge of the NME can give information on the neutrino mass, if the 0νββ decay rate is measured. The novel idea of NURE is to use nuclear reactions of double charge-exchange (DCE) as a tool to determine the ββ NME. In DCE reactions and ββ decay, the initial and final nuclear states are the same and the transition operators have the same spin-isospin structure. Thus, even if the two processes are mediated by different interactions, the NME are connected and the determination of the DCE cross-sections can give crucial information on ββ matrix elements. NURE plans to carry out a campaign of experiments using accelerated beams on different targets candidates for 0νββ decay. The DCE channel will be populated using (18O,18Ne) and (20Ne,20O) reactions by the innovative MAGNEX large acceptance spectrometer, which is unique in the world to measure very suppressed reaction channels at high resolution. The complete net involving the single charge-exchange and multi-step transfers characterized by the same initial and final nuclei will be also measured to study the reaction mechanism. The absolute cross-sections will be extracted. The comparison with microscopic state-of-the-art calculations will give access to the NMEs.

Keywords: neutrinoless double beta decay,magnetic spectrometers
Algorithmic development of proton radiography for image-guided proton radiotherapy of lung cancer. (MSPrad)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: United Kingdom
Call: Individual Fellowships (H2020-MSCA-IF-2020, 2020)
Start date: 2021-09-01
End date: 2023-08-31
Duration (months): 24.0
Total project cost (EUR): 224933.76
EU funding (EUR): 224933.76
Participants: 1
Project status: SIGNED

Proton therapy is a new radiotherapy modality which aims to maximize dose deposition in tumors, while sparing surrounding healthy tissues. It is uniquely suited for the treatment of non-small cell lung cancer, a deadly cancer of current unmet needs. Improving the prognosis of non-small cell lung cancer is an important health and wellbeing milestone, which was identified as one of Europe’s societal challenge in the Horizon 2020 programme. However, the expected benefits of proton therapy are largely impaired by patient motion (breathing) during treatment. A potential solution is to adapt the treatment in real-time by following the location of the tumour with imaging. The overreaching goal of this action is to enable real-time tumor tracking for accurate lung tumor treatment in proton radiotherapy. To do so, a radiographic device, developed by the prospective group, will use the proton treatment source to generate quasi real-time images (proton radiographs) to mitigate the impact of breathing on treatment quality. However, due to the poor image quality of current radiographs, rapid image quality optimization algorithms are mandatory to allow real-time adaptation. This action focuses on producing the necessary algorithms and validation to use proton radiographs in real time. The three main objectives are to (1) develop a proton radiography image quality enhancement (resolution and noise) algorithm based on deconvolution, (2) implement a tumor position tracking algorithm from high-quality proton radiographies, and (3) perform a full experimental validation on the integrated image-guided proton therapy unit. This work will be carried out at University College London (UCL) and its affiliated hospital (UCLH), under the supervision of Prof. Gary Royle and co-supervision of Dr. Charles-Antoine Collins Fekete. It will be a synergistic combination of the applicant’s experience in image reconstruction/analysis and UCL’s expertise on proton physics and therapy.

Keywords: image guided radiation therapy,image reconstruction,algorithmic development
Coherent diffrAction foR a look Inside Nanostructures towards atomic rEsolution: catalysis and interface (CARINE)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Condensed matter physics
Panel description: Structure, electronic properties, fluids, nanosciences, biological physics
Countries involved: France,France
Call: ERC Consolidator Grant (ERC-2018-COG, 2018)
Start date: 2019-11-01
End date: 2024-10-31
Duration (months): 60.0
Total project cost (EUR): 1875000.0
EU funding (EUR): 1793750.0
Participants: 2
Project status: SIGNED

Heterogeneous catalysis of nanoparticles has recently emerged as highly promising way to speed up catalytic processes due to their far higher surface area compared to bulk materials. But they face significant challenges in achieving high catalytic activity and sufficient durability. A key problem has been that all existing approaches to the characterization of atomic scale phenomena in these materials either lack structural specificity or can be employed under highly unrealistic catalytic environments. As an example, operando x-ray catalysis has often been carried out under idealized conditions and averaging information from macroscopic facets. This approach suffers from the lack of transferability to nanocrystalline systems. To tackle this problem, I am developing new state-of-the-art in situ techniques based on coherent x-ray scattering and complementary chemical characterization, with which I will optimize catalyst and reactor operations simultaneously. This is the ambition of the CARINE project to study in situ and operando the structural evolution of catalytic nanoparticles in realistic conditions during reaction by using the unique capabilities of coherent diffraction Bragg imaging (CDI). My proposed work builds on my recent exciting proof-of-concept experiments using Pt nanocrystals that demonstrate the sensitivity and spatial resolution of CDI under liquid conditions. As dedicated instruments for CDI have just reached user operation, it is only now that this new imaging technique can be applied during reaction and can probe structural changes of individual nanocrystals under conditions where up to now, no other techniques could probe the relevant parameters. My project will shed light into most relevant unsolved issues (durability, activity…) that limit the efficiency of today’s industrial processes and will open new horizons with outstanding impact in catalytic research.

Keywords: catalysis,strain engineering,surface,interface,defects
Studying Cancer Individuality by Personal and Predictive Drug Screening and Differential OMICs (SCIPER)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Integrative Biology: from Genes and Genomes to Systems
Panel description: Genetics, epigenetics, genomics and other ‘omics studies, bioinformatics, systems biology, genetic diseases, gene editing, innovative methods and modelling, ‘omics for personalised medicine
Countries involved: Switzerland
Call: ERC Starting Grant (ERC-2018-STG, 2018)
Start date: 2018-11-01
End date: 2024-04-30
Duration (months): 66.0
Total project cost (EUR): 1500000.0
EU funding (EUR): 1500000.0
Participants: 1
Project status: SIGNED

The cellular and molecular systems that determine drug responses in cancer are complex, highly individual, and incompletely understood. As a result, many cancer patients receive ineffective or even harmful therapies, which endangers lives, burdens healthcare systems, and prevents new therapies from reaching clinical approval. To address this problem, we are developing a platform that measures hundreds of ex vivo drug responses from small patient biopsies by immunofluorescence, automated confocal microscopy, single-cell image analysis, and machine learning. We preserve cellular memory and maximize physiological relevance by not culturing or sorting cells prior to drug exposure. Sub-cellular, single-cell, and cell population-wide image analysis reveals on-target drug responses and disentangles multicellular ones. In a first interventional clinical trial, this phenotypic information alone led to strongly improved treatment of patients with aggressive hematologic malignancies. Enabled by this high-throughput, predictive, and phenotypic information, I here propose to identify the molecular and cellular systems that govern treatment response individuality in cancer. (Aim 1) We will combine drug response profiling with RNA sequencing and proteomic measurements of malignant and healthy cells from the same biopsies. Critically, the patient-internal comparisons in both screening and OMICs allow neutralizing complex confounding factors. (Aim 2) New multiplexed immunofluorescence and convolutional neural network-based analyses will identify multiclass cell-types and -states, and quantify non-cell-autonomous responses. (Aim 3) Computational integration and causal inference will identify the molecular determinants and governing principles of drug response individuality in cancer, amenable to further validation. This proposal will thus improve our mechanistic understanding of cancer individuality and develop powerful new tools for OMICs-based precision medicine.

Keywords: image based screening,personalized medicine,machine learning
Deep Gaussian Processes for Geostatistical Data Analysis (DeepGeo)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: United Kingdom
Call: Individual Fellowships (H2020-MSCA-IF-2017, 2017)
Start date: 2018-07-23
End date: 2020-07-22
Duration (months): 24.0
Total project cost (EUR): 183454.8
EU funding (EUR): 183454.8
Participants: 1
Project status: CLOSED

Urban soil contamination resulting from former land-use is important but challenging to measure. Direct measurements are expensive and time-consuming to acquire, making a city-wide assessment impossible. Current statistical methods for modelling the distribution of pollution in urban environments, such as kriging, often fail to do so properly, since the contamination is highly local and uncorrelated with the surroundings. The problems can be mitigated by using multi-output models, such as co-kriging, where several datasets are modelled concurrently. The methods are, however, slow to train and have limited flexibility. DeepGeo will develop state-of-the-art methods for assessing urban soil contamination and provide an open-source software library for geostatistical data analysis, directly making the novel discoveries available to a wide audience. DeepGeo aims to solve the mentioned problems by the use of deep Gaussian processes for estimating urban soil pollution. This recently developed class of models promises enormous flexibility and can model highly nonlinear correlations between outputs, making them far superior to standard co-kriging. They do, however, suffer from scalability issues and empirical studies show flexibility issues with increasing depth. DeepGeo will address the scalability issue by developing new algorithms for approximate inference and for inducing sparsity. Inspired by recent advances in training of deep neural networks, specialised covariance functions that allow for deeper Gaussian process architectures will be constructed. Finally, new and improved methods for learning complicated correlations between outputs will be investigated, thus increasing the amount of information that can be gained from already available data. By making the developed methods available as open-source software, DeepGeo seeks to reach a broad range of research fields as well as benefitting the geochemical industry.
Storage energy UNit for Smart and Efficient operation on Tarmac (SUNSET)

Framework: Horizon 2020 — Societal Challenges
Programme: Smart, green and integrated transport
Theme: Public-private partnerships
Destination: Clean Sky 2
Action type: Innovation actions
Partnership: CS2 (Art-187)
Countries involved: France
Call: High density energy storage module for an electric taxi (H2020-CS2-CFP06-2017-01, 2017)
Start date: 2018-03-01
End date: 2021-11-30
Duration (months): 45.0
Total project cost (EUR): 1088600.0
EU funding (EUR): 800663.0
Participants: 2
Project status: CLOSED

The steady growth of global air traffic passenger demand requires the air transport industry to work even harder to improve the associated levels of safety, efficiency, and environmental performances of aircrafts. As such, the transient to more electrified aircraft systems is strongly encouraged throughout the complete aircraft operational behaviour, including on-ground operations. Indeed, on-ground operations are still mostly engine based, the main engines designed for flight phases at high power levels are thus used as well as power source to move the aircraft on ground. This induces major economic and environmental losses: currently, fuel consumption from taxi operations is estimated to cost 6,4billion€ and to reach 18M metric tons of CO2 emission per year. To reduce unnecessary fuel burn and their related emissions, a technological alternative has already been identified: Electric Taxiing System (e-Taxiing) . However, some technical bottlenecks, as the one dealing with the solution storage energy capacity, have still to be overcome before enabling those system to be used by all existing and future commercial aircrafts. SUNSET will target this specific technical challenge proposing a high performances energy storage module development connected to the future e-Taxiing system. The SUNSET technology will also address the related challenge of mass reduction by providing a high-density energy recovery capability (30Wh/kg) to perform aircraft electrical decelerations while also minimizing cooling and weight. SUNSET partners, Centum Adeneo and Ampère Laboratory (UCBL) are part of the European recognised air industry value chain and will as such be involved in both development of the SUNSET solution with their Topic Manager support for its integration in the e-Taxiing system. SUNSET project will therefore contribute to bring out an innovative solution enabling a winning differentiator for European aircraft manufacturers.

Keywords: green taxi system,energy storage module,high environmental benefits
Tackling Idleness and Under-utilization of Heavy-Machinery with a new fully Automated and Versatile Docking System (DOCKNROLL)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Austria
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2018)
Start date: 2019-03-01
End date: 2019-06-30
Duration (months): 4.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

30-40% of all heavy machinery is standing idle while the motor is running which is a major waste and health hazard, but there are currently no solutions to address this problem. Major causes of idling are 1)specialized machines not being utilized due to seasonality: besides having to cope with the maintenance of these machines, users may have to look for others when needing a different application;2)the driver having to leave the cabin and dock the vehicle to attach components manually (time & cost-intensive, unsafe process), while the engine is running. We, SYN TRAC GmbH are an Austrian start-up company founded with the aim to bring to the market a solution that would tackle both problems described - DOCK’N’ROLL. DOCK’N’ROLL is a heavy machinery vehicle which can be indefinitely augmented with customized attachments (crane, snow plower, etc.) through our innovative docking system (patented). Heavy machinery operator companies can finally have access to a fully transformable vehicle, which can rapidly couple with attachments depending on the desired application, at the same time it provides safety to drivers. Also, it is built to provide the highest performance and reduced emissions and is the only vehicle that provides full docking automation. We have now finished producing our first prototype and signed our first customer deal. Through this project we seek to finalize DOCK’N’ROLL’s development, to prepare the scale-up of our production from 20 to 50 units/year to meet our confirmed demand in European countries, as well as to consolidate our supplier and distributor network. In November 2017 we made our first appearance at the World’s leading International Fair of the Sector – Agritechnica – rendering 360 contacts and +150 leads from European companies. This confirmed customer interest serves as a base to our initial estimations to reach €28.9M in revenues and to grow our team to 55 people by 2025.

Keywords: heavy machinery,docking system,attachment,truck,snow plower,crane
Best in class Deep Learning Predictive Model (BEYOND)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Italy
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2018)
Start date: 2018-06-01
End date: 2018-11-30
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

HPA S.r.l. has reimagined the way predictive analytics services are made today, with a new Forecast-as-a Service model, based on cutting-edge deep learning algorithms that proved wide applicability in multiple vertical industries. After validating the problem/solution fit, HPA has engaged 3 prospects in the Energy, Technology and Media forecasting domains to prepare some Proofs of Concept and benchmark the solution. It’s also worth pointing out that no similar tools exist in the case of the second and the third verticalizations. The novelty of this solution consists of the following unique selling points: 1.BEYOND predictive models outperforms most used competitor solutions in terms of accuracy (on average, 5% of error against 12-38% of errors by competitors) and speed. An early adopter of BEYOND operating in the energy trading market, demonstrated that increased accuracy provided by our model can result in a yearly operating margin increase of at least €120.000. 2.BEYOND will overcome adoption and usage barriers for many businesses providing easy-to-use models that require little knowledge from users with different skill levels from beginners to experienced analysts. 3.As a Forecast-as-a Service model, BEYOND will be offered with a flexible pricing strategy that will allow installation costs drop in a range between 50 to 90 % and long term costs decrease by a minimum of 10% up to more than 70%. Thanks to the SME Phase1 funding, HPA will be able to: a) finalize the Minimum Viable Product (i.e. development of programming language custom libraries, output formats and integration of third-party datasets); b) design the web application; c) assessment of more industry verticalizations and the search of corresponding prospects; d) set-up a massive promotional/communication plan to reach the global market during the Phase 2. With a SME Phase 2 funding we will be able to massively market BEYOND, reaching a € 10MLN turnover and 26 jobs by 2023.

Keywords: deep learning,energy forecasting models,technology forecasting,media forecasting,publishing forecasting,editorial forecasting,time series analysis
Emergence of novel phenotypes in co-evolving biological systems: allelic diversification and dominance at the Self-incompatibility locus in Arabidopsis. (NOVEL)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Environmental Biology, Ecology and Evolution
Panel description: Ecology, biodiversity, environmental change, evolutionary biology, behavioural ecology, microbial ecology, marine biology, ecophysiology, theoretical developments and modelling
Countries involved: France,France
Call: ERC Consolidator Grant (ERC-2014-CoG, 2014)
Start date: 2015-10-01
End date: 2021-05-31
Duration (months): 68.0
Total project cost (EUR): 1760880.0
EU funding (EUR): 1760880.0
Participants: 2
Project status: CLOSED

The emerging field of systems biology is revealing the intricate nature of biological organisms, whereby a large fraction of their individual components (genes, proteins, regulatory elements) interact with several others. The co-evolutionary processes that this entails raises the question of how phenotypic novelty may arise in the course of evolution, since all parts of the system have to evolve in a coordinated manner if the phenotype is to remain functional. For most biological systems, however, we are lacking even basic insight into the fine-scale mechanistic constraints and the underlying ecological context. In this project, we will focus on the sporophytic self-incompatibility system in outcrossing Arabidopsis species, a model biological system in which two distinct co-evolutionary processes are becoming well-understood: 1) between the male and female reproductive proteins allowing self-pollen recognition and rejection and 2) between small non-coding RNAs and their target sites that jointly control the dominance/recessivity interactions between self-incompatibility alleles. By studying these two model systems, we will aim to catch the emergence of functional and regulatory novelty in flagrante delicto. We will take a multidisciplinary approach combining theoretical and empirical population genetics, evolutionary genomics and ancestral protein resurrection using transgenic plants. Our goal is threefold: 1) decrypt the molecular alphabet of the interaction between co-evolving nucleotide sequences, 2) predict and evaluate the fitness landscapes upon which the two co-evolutionary processes are taking place and 3) exploit natural variation in closely related species to unveil the kind of co-evolutionary process in natural populations. Our combination of various powerful approaches in a tractable model system should provide insight on diversification, a poorly understood but fundamental evolutionary process that is taking place at all levels of organization.

Keywords: plant genomics,self incompatibility