I see myself as a hardworking and dependable individual. I am highly motivated to acquire new knowledge as well as experience and grow as a result. Furthermore, I consider myself adaptable and communicative, and I strive to assist others with their problems. One of my core strengths is being demanding of myself. On the other hand, my biggest weakness is having to improvise, so I've developed good planning skills. My primary area of expertise is Artificial Intelligence (AI) applications in power systems, smart grids, and industrial/household environments. With a greater emphasis on improving energy efficiency, renewable energy use, demand response participation, bill savings, machine degradation, and product quality. On July 13, 2020, I completed my degree in Informatics Engineering at ISEP with a 17 out of 20 grade. Accordingly, I received a merit certificate in 2021 for being among the best students. Afterward, I completed my master's degree in AI Engineering at ISEP on November 15, 2022, with a 19 out of 20 grade (20 out of 20 on my master’s dissertation). I received the second-highest grade, and as a result, I received a best student merit certificate in 2023. From the start of my Informatics Engineering degree final project on 3 February 2020 up until 29 February 2024, I worked on GECAD as a researcher. My research focused on the development and improvement of a system capable of scheduling production lines by using a Genetic Algorithm. In addition, it also enabled the rescheduling of a previous plan to respond to demand response programs and machine breakdowns. Moreover, predictive maintenance using Machine Learning (ML) was also explored and implemented. During this time, I worked on two international European projects: SPEAR in 2020, and MUWO in 2021-2023. These international projects were an invaluable experience in my career, since they allowed me to collaborate with various businesses in the manufacturing sector, thereby improving my international dissemination skills and networking. For now, I have published 7 papers in scientific journals, 1 in book chapters, and 8 conference papers. Additionally, 2 manuscripts have been submitted. Regarding scientific journals, I have published in Energy (Elsevier, IF 9.00), Renewable Energy (Elsevier, IF 8.70), Energy Reports (Elsevier, IF 5.20), IEEE Access (IEEE Xplore, IF 3.56), Energy Informatics (Springer, IF 3.52), and Energies (MDPI, IF 3.20). A book chapter was published in the Intelligent Data Mining and Analysis in Power and Energy Systems: Models and Applications for Smarter Efficient Power Systems book. My most relevant paper is a recently published work on residential load shifting for cost reduction (DOI:10.1016/j.energy.2022.124978), for which I was awarded a merit certificate in scientific publication. At the moment, since the beginning of March of 2024, I have been working on my PhD at USAL, which was awarded by the FCT and ranked first in Computer Science and Informatics among 147 candidates. The proposed PhD, titled “Towards Efficient and Fast-paced Manufacturing Production Lines by applying Automated Machine Learning Models with Explainable Artificial Intelligence for Knowledge Transfer”, focuses on the research, design, implementation, validation, and dissemination of easily deployable and interpretable ML models for manufacturing environments capable of improving a production line scheduling algorithm’s reliability and efficiency. It will achieve this by employing an Automated ML (AutoML) methodology in a manufacturing environment, for faster deployments of ML systems without the need for an expert, and eXplainable AI (XAI), to generate explanations for the model's actions. The main novelty of the PhD is the usage of ML models based on AutoML and using XAI to obtain knowledge that then can be applied in a production line scheduling algorithm, through knowledge transfer. My PhD is being carried out both in GECAD (ISEP), and BISITE (USAL).