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University of Malaga
193 Projects, page 1 of 39
  • Funder: European Commission Project Code: 101109529
    Funder Contribution: 165,313 EUR

    Reducing greenhouse gas (GHG) emissions is a worldwide priority and one of the Horizon Europe Missions. Smart cities, e-banking, industrial automation, and the Internet of Things (IoT) – together with other multiple services enabled by mobile communications – contributed to reduce around 2,135 million tonnes of GHG emissions in 2018, giving raise to the so-called enabling effect of mobile technologies. 5G and 6G are expected to even increase this effect by delivering an unprecedented fabric of massive connectivity to millions of users and interconnected devices. Paradoxically, despite being more efficient in terms of transmitted bits per joule, a 5G cell could consume up to 140% more energy than a 4G one for covering the same area, mainly due to the use of massive antenna arrays, higher frequency bands and high base station (BS) density. With 73% of the total energy consumed in the radio access network, designing more efficient BS hardware and an energy-aware network design arise as mandatory directions. In this project, an energy efficient design of 5G and 6G networks will be addressed. First, the use of the recently proposed dynamic metasurface antennas (DMAs) will be explored as alternative to conventional arrays, characterising the energy savings provided by these structures. Second, intra-cell (turning off parts of the DMA at the BS) and inter-cell (switching off entire BSs) sleep modes algorithms will be designed for low load periods of time, accounting for the interaction between them while meeting quality of service constraints. Finally, the proposed solutions will be validated, and the benefits with respect to conventional and state-of-the-art approaches.

  • Funder: European Commission Project Code: 799078
    Overall Budget: 239,191 EURFunder Contribution: 239,191 EUR

    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).

  • Funder: European Commission Project Code: 101108024
    Funder Contribution: 165,313 EUR

    The design of efficient catalysts is vital for developing advanced catalytic processes, which allows to switch to more sustainable industry. Single Atoms Catalysts has caught all the attention within the field of catalysis owing to their outstanding catalytic performance, being potential efficient systems. However, their applications in industry is hindered by the low metal loading required and by the lack of an environmentally friendly, simple and inexpensive batch preparation method. Ecofriendly and COst-effective preparation of Single Atoms Catalysts (ECOSACs) aims to synthesize several SAC formulations using high metal loading through a new and innovative mechanochemistry route, allowing their batch preparation in a near future. The specific goals of ECOSACs are: (i) use of continuous flow mechanochemistry reactor for the synthesis of given SACs formulations, mainly composed by reducible metal oxides supported noble (Pt,Au) and earth abundant (Co,Fe) metals; (ii) verify the versatility of this new synthesis method; (iii) determine the physicochemical properties and electronic environment of the SACs by using traditional and advanced characterization techniques; (iv) determine the catalytic activity of SACs in selective oxidation reaction of biomass derived molecules and (v) develop an understanding of the relationship between physicochemical properties and the activity and stability of SACs . The results of this project have the potential to introduce a green and low-cost continuous preparation of SACs and thereby develop more sustainable and greener European and global industry sectors, being align with the European Green Deal program, area 3 “Industry for a Clean and Circular Economy”. Throughout this project I will learn new techniques relevant to industrial catalysis, develop my skills as an independent researcher and mentor, and expand my network to include international collaborations and relationships as transition to an established researcher.

  • Funder: European Commission Project Code: 327197
  • Funder: European Commission Project Code: 234808

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