Project description
Training researchers in grey box models
Mobility solutions are shaping the future, putting the human experience at the centre of transport and across all modes and infrastructures. The next step is to sustain the ongoing transition of European personal mobility towards safe and reliable intelligent mobility systems. The EU-funded GREYDIENT project will establish an innovative training network for the next generation of early-stage researchers (ESRs). The focus will be on grey box models, which are an answer to the pressing issue at hand as they are aimed at optimally integrating (black box) data driven machine learning tools with (white box) simulation models. The ESRs will be trained in the modelling, propagation and quantification of relevant variabilities, the application of Big Data and machine learning methods as well as the optimal combination of data-driven approaches with numerical models.
Objective
The GREYDIENT innovative training network aims at training a next generation of Early Stage Researchers (ESR) to fully sustain the ongoing transition of European personal mobility towards safe and reliable intelligent mobility systems via the recently introduced framework of grey-box modelling approaches. One of the main challenges that we currently face in this context is the integration of the data captured from the plenitude of sensors that are involved in a particular road-traffic scenario, ranging from monitoring car-component loading situations to power network-reliability estimations. The aim is to fully exploit the potential of merging these data with advanced computational models of components and systems that are widely available in industry in order to fully assess the momentarily safety. Grey box models are an answer to this pressing issue, as they are aimed at optimally integrating (black-box) data driven machine learning tools with (white-box) simulation models to greatly surpass the performance of either framework separately. However, the training of professional profiles in Europe who combine knowledge and experience in state-of-the-art data-driven black box and numerical white box approaches with expertise in methods for reliability and safety estimation is scarce. Therefore, GREYDIENT will train its ESR’s in a wide spectrum of fields, including the modelling, propagation and quantification of the relevant variabilities, the application of big data and machine learning methods, as well as the optimal combination of data-driven approaches with numerical models. All our ESR’s will obtain a PhD from an internationally respected University, build experience in communicating and disseminating their work, applying their research skills in a non-academic context and receive in-depth training in transferable skills such commercialization, collaboration and entrepreneurship. This training will be organized in close cooperation with key industry stakeholders.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- social sciences economics and business business and management entrepreneurship
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering sensors
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.1. - Fostering new skills by means of excellent initial training of researchers
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-ITN - Marie Skłodowska-Curie Innovative Training Networks (ITN)
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-ITN-2020
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
3000 LEUVEN
Belgium
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.