Development of an Airborne Wind Energy System (AWES) prototype, more precisely a ground controlled two-line kite. Main responsibilities:- Development of the telemetry gathering software.- Development of the control system software, including the actuator and control joystick interface.- Flight data analysis and postprocessing, application of machine learning techniques to obtain a data-driven model of the system.- Currently working on a parametric AWES design software intended to be used as a conceptual design tool, including aerodynamic, structural and stability analyses.

Created an application suite to perform structural analysis tasks.- In this position the programs’ performance was critical due to the tremendous amount of data to be processed. Managed to cut computing times by 50% by implementing vectorized operations.- Intensive use of common Python data analysis packages, e. g., Pandas, Numpy, Matplotlib etc.

Responsible for the development of AI models for computer vision and speech recognition. The aim of the project is to detect the amount of stress and workload capacity of users in a bus-driving simulator.The main tasks are:- Implementation of several machine learning techniques (Neural networks, logistics regression, SVM etc.)- Real time applications development with Python- Integrating the software with Raspberry Pi

The main tasks performed in this position are:- Functional and unit testing of safety-critical software- Safety-critical software development- Produce certification documents within the frame of DO-178C/DO-330

Aircraft performance software development for Airbus Defence and Space.

Bonded joints stress analysis and failure mode analysis of adherends under combined loading.

Internship during which I contributed to develop offshore wind turbine dynamic models .