Wilgo Moreira Nunes Cardoso received his BEng Mechanical Engineering degree in 2008, graduating from Federal University of Ceara (a 5 years course, Brazil) with a grade of 7.8/10. Following his BEng degree, he then completed a Master’s degree in 2012 at the Federal University of Uberlandia (Brazil), specializing in mechatronics and cable-driven rehabilitation robotics. In 2023 he started the PhD in Electrotechnical Engineering and Intelligent Systems at the University of Coimbra. To date, Wilgo has authored 7 peer-reviewed conference publications and is currently involved in a research and development (R&D) project. His Master’s degree has been officially recognized in Portugal with a final classification of 18 out of 20, reflecting academic excellence. Currently he investigates deep learning-based perception for agricultural robotics, with an emphasis on uncertainty-aware models and probabilistic segmentation techniques using multispectral data. Beyond his academic experience, Wilgo brings over ten years of engineering experience in companies, having held technical leadership positions at Petrobras Distribuidora S.A. and the Brazilian Hospital Services Company (EBSERH). In these roles, he coordinated multidisciplinary engineering projects in automation, sustainability, and HVAC systems, including critical interventions during the COVID-19 pandemic. Such experience background underscore his ability to deliver technically sound and socially impactful solutions. In his early doctoral work, he has already co-authored and presented papers at international conferences (e.g., ROBOT 2024, ICIR 2024), evidencing scientific maturity and early research productivity. He maintains an active presence in the open science community through his GitHub profile (github.com/wilgomoreira), where he shares machine learning, image segmentation, and robotics projects, reinforcing his commitment to research transparency and reproducibility. Wilgo currently collaborates with the VESTA project, coordinated by the SME SpotLite, where he has been developing new approaches on supervised classification using satellite imagery and machine learning, contributing to the development of more explainable and robust AI models. This collaboration bridges academic research and industry application, aligning with the goals of non-academic doctoral environments and strengthening the societal relevance of his PhD. Combining a consistent academic path, complementary industry/SME experience, and a deep commitment to research and innovation.