Extended Reality (XR) refers to the technology which creates a 3D immersive environment where a user can perceive and interact with virtual objects by means of a head mounted display. Even though XR is still an active research area, its European market has already reached an estimated worth of €34 million, employing up to 480,000 people. It is expected that a mature XR infrastructure will raise the standards for remote working by enabling functional virtual workspaces, which could stimulate the European economy by offering equal opportunities to workers regardless of geography, and reduce greenhouse gas emissions due to less commuting, which is in line with European goals. A key part of XR is spatial audio, or sound signals which the user perceives as if they come from specific locations in a 3D space. Realistic spatial audio can be computationally expensive, particularly when simulating reverberation, i.e. the interaction of a sound source and the environment. This project investigates how humans perceive spatial reverberation to build more efficient rendering methods which enable high-quality spatial audio on XR. To that end, a novel reverberation encoding technique is proposed, based on a variable order Ambisonics framework, and a model for sound localisation in reverberation will be developed, which is expected to be a significant advancement in the field of auditory perception. The project will take place at a leading multidisciplinary group specialised on real-time spatial audio processing and who can offer excellent international collaboration opportunities. The researcher will bring skills on evaluation of spatial reverberation and inter-sectoral experience from having worked at a world leading research group within the industry, which will facilitate the transfer of knowledge. The proposed work will expand the researcher’s experience, competencies and professional networks, potentiating the development of his career as an independent researcher.

Sustainable agriculture is an ambitious concept conceived to improve productivity but minimizing side effects. Why the efficiency of a biocontrol agent is so variable? How can different therapies be efficiently exploited in a combined way to combat microbial diseases? These are questions that need investigation to convey with criteria of sustainability. What I present is an integral proposal aim to study the microbial ecology and specifically bacterial biofilms as a central axis of two differential but likely interconnected scenarios in plant health: i) the beneficial interaction of the biocontrol agent (BCA) Bacillus subtilis, and ii) the non-conventional interaction of the food-borne pathogen Bacillus cereus. I will start working with B. subtilis, and reasons are: 1) Different isolates are promising BCAs and are commercialized for such purpose, 2) There exist vast information of the genetics circuitries that govern important aspects of B. subtilis physiology as antibiotic production, cell differentiation, and biofilm formation. In parallel I propose to study the way B. cereus, a food-borne pathogenic bacterium interacts with vegetables. I am planning to set up a multidisciplinary approach that will combine genetics, biochemistry, proteomics, cell biology and molecular biology to visualize how these bacterial population interacts, communicates with plants and other microorganisms, or how all these factors trigger or inhibit the developmental program ending in biofilm formation. I am also interested on knowing if structural components of the bacterial extracellular matrix (exopolysaccharides or amyloid proteins) are important for bacterial fitness. If this were the case, I will also investigate which external factors affect their expression and assembly in functional biofilms. The insights get on these studies are committed to impulse our knowledge on microbial ecology and their biotechnological applicability to sustainable agriculture and food safety.

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).