Statistical and Deep Learning to improve the desing of new materials for selective reaction of energetic interest
There is a huge interest on the study and design of new materials and their application on selective reactions of energetic interest for the sustainable fuels production and energy storage via artificial photosynthesis (CO2 reduction and water splitting). There is a vast body of literature about how to design and characterizations these new materials, both theoretical and experimental, which represent a huge labelled dataset
Hybrid materials based on graphene and organo-inorganic compounds for energy source applications
In the context of the Organic Device Characterization Laboratory-LabCADIO (belonging to the laboratory network of the Regional Government of Madrid, ref-351), led by Prof. Carmen Coya, deals with the development of materials, the manufacture of devices, the optimization of cost-effective processes for Organic Electronics (special attention to patterning and transformation of 2D materials (i.e Graphene) by electro erosion) and simulation of transport processes in organic electronic devices.
Energy-efficient wireless systems for communications networks
New generation mobile networks (5G and its alternatives) are expected to become a game changer that will certainly modify the way people participate in society and benefitiate from all sorts of public and private services. However, it also implies an important increase in the density of wireless communications systems. As side effects, the energy consumption and the electromagnetic contamination are going to be dramatically increased. This drives to different situations in urban and rural areas. Both would benefitiate from a significant reduction in the power consumption of wireless systems, and urban areas would also obtain important benefits from a significant reduction in radio emisions. The proposed research aims at proposing a new system architecture for wireless systems in which hardware is powered and activated upon demand depending on the service level, bandwidth and other requirements that must be satisfied
Adaptive optimization and learning over networks
Ours is a connected world. Technological (power, communications and transportation) networks play a leading role in our daily lives. To cope with the technological challenges posed by the modern society, contemporary networked systems have became more flexible, involved and autonomous. Power and transportation networks are now equipped with batteries and have access to renewable sources of energy. This evolution has opened the door to more energy efficient operation schemes and to better user’s experience. However, it has also made the design, management and operation of the networks more difficult. Successful execution of those tasks requires a detailed modeling and analysis of the network and its terminals.