Research Fellow Applied AI

Benefits

Additional Information

R0001486 Research Fellow Applied AI (Open) We're Nadara. We work harmoniously with the power of nature and the communities around us to power lifetimes to come. We call our approach 'living energy'. We don't just produce renewable energy, we live it - recognising our relationship with the people touched by our business and supporting social, educational, cultural, and environmental initiatives that contribute to the development of the communities we work alongside. Discover more about our business here: https://nadara.com/about/ Nadara is an innovative place to work. We work in a stimulating and challenging environment, where every day we explore the unknown with curiosity, make decisions with quality and take action and deliver with courage. For us diversity is a real value, and we encourage in connecting different perspectives with respect. Discover more about our culture and approach here: https://nadara.com/living-the-company/ Job Description Summary: The Research Fellow Applied AI will contribute to Nadara's participation in Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks, within the Horizon Europe framework. The role focuses on applied artificial intelligence and data science methodologies for advancing the digitalisation, automation, and sustainability of renewable energy systems. Embedded in Nadara's Innovation & R&D function, the Fellow will work across interdisciplinary projects involving academic and industry partners, aiming to develop novel algorithms, data-driven tools, and intelligent systems that enhance the performance, monitoring, and decision-making processes in renewable energy operations. This position bridges early-stage research with industrial application and supports Nadara's strategic commitment to innovation through collaborative R&D. Job Description: Key responsibilities and Authorities Design AI-enabled methodologies for automated processing of sensor data from wind turbines. Propose optimised sensor network architectures for both bottom-fixed and floating offshore wind farms. Develop data fusion techniques that integrate heterogeneous sources (e.g., operational, mechanical, structural). Construct monitoring databases linking operational variables with mechanical and structural responses. Apply machine learning and statistical approaches to suggest automated maintenance and operation strategies. Contribute to the development of digital tools for remaining useful life estimation and decision-making support. Liaise with academic and industrial partners within the MSCA DN consortia. Report research progress and participate in project reviews and doctoral training events. Ensure alignment with data governance, IP, and research ethics as per Horizon Europe requirements. Participate in scientific dissemination, including journal publications and conference presentations. Support internal uptake of research results by Nadara's Innovation and Digital Transformation and O&M teams. Health & Safety, Quality and Environment (HSQE) Key responsibilities and Authorities This document must be read in combination with the HSQE Management System Manual, available on Nadara Intranet, which outlines the mandatory HSQE responsibilities and authorities related to this role. Technical and cross Competences (Optional) Experience in AI/ML applied to time series, sensor data, or engineering applications. Solid programming skills in Python (e.g., PyTorch, scikit-learn, pandas). Knowledge of signal processing, feature engineering, and data analytics. Familiarity with onshore/offshore wind systems or structural health monitoring (desirable). Proficient in collaborative research tools (e.g., Git, Jupyter Notebooks). Fluency in English (spoken and written). Managerial skills and behaviours (Optional) Strong communication and stakeholder engagement skills. Ability to work independently while contributing to a coordinated research agenda. Analytical thinking, self-motivation, and scientific integrity. Education and Qualifications Mandatory: MSc degree in Applied AI, Data Science, Engineering, Computer Science, or related field. Enrolled or eligible to enrol in a Doctoral Programme in collaboration with relevant Universities. Desirable: Prior experience with EU-funded R&D projects or research networks. Background in data science, mechanical or civil engineering applied to wind energy. Travel Requirements (Optional) Participation in international training events, secondments, and consortium meetings across Europe. Occasional site visits to wind assets may be required. Location: Lisbon Time Type: Full time Worker Subtype: Fixed Term (Fixed Term)