Objective
Nowadays, intelligent systems based on deep learning (DL) are latent in many aspects of our society. But the use of inadequate neural networks (NNs) architectures and the high computational costs required by DL limit its widespread use. Thus, advanced optimization methods (such as metaheuristics) may be applied to improve common DL methodologies, which in general use gradient based methods and apply complex engineering by hand.
This project aims to define an efficient DL methodology, which is named Neural CO-evolutionary Learning (NeCOL), based on the marriage between co-evolutionary algorithms (CEAs) and recurrent NNs (RNNs). NeCOL will be used to automatically define RNNs of high (unseen) efficiency and efficacy, which will be adapted to explicit needs. It will be applied in two use cases of the highest value and relevance in EU: cybersecurity and Smart City. We focus on RNNs because they are applied to non-stationary data streams, as in our use cases.
Despite EU efforts, China and the USA are the most productive countries in DL. Thus, EU must try harder to lead this compelling domain. This MSCA will support the candidate to master this new cutting-edge world-wide research, which will contribute to EU excellence and competitiveness. It will allow the candidate to get exceptional trainings from world class experts at the prestigious MIT that will be exploited at UMA and the priceless supervision of Prof. Alba (UMA) and Prof. O’Reilly (MIT).
The applicant is the appropriate choice to successfully accomplish this research because he has a valuable expertise in modeling hard-to-solve real-world problems (as it is the case of RNNs optimization) and addressing them by using metaheuristics. The expected early high scientific impact of this research in the EU will open up the best possible career opportunities for him, preparing him to overwhelmingly compete for a solid permanent position at UMA and other possible destinations (even industry).
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: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- engineering and technology civil engineering urban engineering smart cities
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
- natural sciences computer and information sciences computer security
- natural sciences computer and information sciences artificial intelligence computational intelligence
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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.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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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-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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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-IF-2017
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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.
29016 MALAGA
Spain
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.