Applied AI Research Fellow — Wind Energy & Data Science

The Nadara group; Lisboa; Híbrido; EUR 42 000 - 54 000; Tempo integral

Resumo da oferta

The Nadara group in Lisbon is seeking a Research Fellow in Applied AI to contribute to MSCA Doctoral Networks within Horizon Europe. You will develop AI and data science solutions for renewable energy systems, bridging early research with industrial applications and supporting Nadara’s innovation strategy.

You will work across interdisciplinary projects, design sensor data processing methods, build data fusion pipelines, and contribute to digital tools for O&M decisions, with emphasis on data

Qualificações

• 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.

Responsabilidades

• Design AI-enabled methodologies for automated processing of sensor data from wind turbines.
• Propose optimised sensor network architectures for offshore wind farms.
• Develop data fusion techniques that integrate heterogeneous sources.
• Construct monitoring databases linking operational variables with mechanical responses.
• Apply machine learning and statistical approaches to maintenance and operation strategies.
• Support internal uptake of research results by Nadara’s Innovation & R&D teams.

Conhecimentos

Python

ML/AI

Time series

Data analysis

Git

Jupyter

Formação académica

MSc in Applied AI

Ferramentas

PyTorch

scikit-learn

pandas

Descrição da oferta de emprego

The Nadara group in Lisbon is seeking a Research Fellow in Applied AI to contribute to MSCA Doctoral Networks within Horizon Europe. You will develop AI and data science solutions for renewable energy systems, bridging early research with industrial applications and supporting Nadara’s innovation strategy.

You will work across interdisciplinary projects, design sensor data processing methods, build data fusion pipelines, and contribute to digital tools for O&M decisions, with emphasis on data