This Master's Thesis focuses on developing and automating Machine Learning models for Telecommunications (TLC) networks, specifically leveraging real-world operational data from Nokia's Microwave Radio technology to enhance high-capacity wireless backhauls and fronthaul. Throughout this journey, you will benefit from the guidance of an AI expert. This expert will provide structured guidance and continuous technical support for both the solution's development and the preparation of your Master’s thesis.

You Have:

• Courses in Artificial Intelligence (AI) and Machine Learning (ML)Python programming skills with familiarity in standard Machine Learning frameworks
• Knowledge of data manipulation and preprocessing techniques
• Strong analytical thinking and the ability to translate methodological choices into measurable performance improvements
• Good English spoken and written
• Focus, commitment, and reliability, enabling effective interaction within multicultural and multilingual environments across multiple geographical sites

It would be nice if you also had:

• Prior knowledge of the telecommunications domain
• Strong motivation to learn domain-specific concepts, actively engage with real industrial data, propose ideas, and develop critical thinking in the context of emerging technologies

Your core responsibility will be the creation of a Machine Learning model. This model will be trained, tested, and integrated over a Nokia AI-based network analysis platform, contributing directly to our innovative solutions. This project is structured around three primary objectives:

• First, you will engage in an in-depth data discovery phase. This involves exploratory analysis and applying classical statistical methods to extract meaningful insights, define robust data preparation strategies, and guide effective feature engineering.

• Second, the project focuses on the design, development, validation, and optimization of a Machine Learning model. This stage demands methodological rigor in model selection and thorough performance evaluation to ensure high quality and effectiveness.

• Third, you will be responsible for the refinement of the codebase. This includes improving its usability and reproducibility, with particular attention to automation strategies to ensure scalability and effective deployment within the Nokia ecosystem.