Developing and evaluating statistical and machine learning models to detect outliers in sensor data from wind turbines within wind farms.Exploring the potential of Large Language Models (LLMs) to validate, refine, and enhance data within the realm of wind energy.

Contributing to the development of a cloud data platform for various use cases in the field of renewable energy. Main contributions to one of the use cases include collecting and storing data, preprocessing data, processing data by cleaning, transforming and aggregating as well as normalizing data to match an industry standard. Further contributions besides ETL procedures refer to the platform itself and include operating and maintaining the platform in accordance to the practices of DevOps (and of the SCRUM framework).

Exploring and benchmarking convolutional neural networks at the cutting-edge of instance segmentation to improve a smart city security service.

Forecasting business client's energy consumption in order to detect outliers by using statistical methods and random forests.

Applying reinforcement learning to train an autonomous car (Deep Racer by Amazon Web Services).