Function and Responsibilities:

• Conducting scientific research in the field of GNSS Reflectometry and implementing machine learning techniques.
• Development and implementation of algorithms for planning, analyzing, and interpreting GNSS Reflectometry measurements.
  
• Development of new GNSS Reflectometry methods based on machine learning and extensive scientific, mathematical/physical studies, focusing on meteorological applications such as measuring wind speed/direction and potentially precipitation over oceans, soil moisture, and snow/ice coverage, considering operational aspects, e.g., for use in improving weather forecasts, and the integration of new satellite systems like Galileo and Beidou.
  
• Evaluation, analysis, and interpretation of GNSS Reflectometry measurements and physical models:
  Development of concepts for validating and calibrating the developed models with independent data sources; quality control of data products from GNSS Reflectometry measurements through these comparisons and independent quality parameters.     Derivation of meteorological parameters from GNSS Reflectometry measurements over oceans, ice, and land surfaces.

Education:

M.Sc Geomatics Engineering, University of Stuttgart, Stuttgart, Germany, 2021

Projects:

Artificial Intelligence for GNSS Reflectometry: Novel Remote Sensing of Ocean and Atmosphere (AI4GNSSR), a Helmholtz AI project, utilizes advanced artificial intelligence techniques in conjunction with Global Navigation Satellite System Reflectometry (GNSS-R) for monitoring oceans and atmosphere. This project aims to improve the accuracy, depth, and utility of geophysical data derived from spaceborne GNSS-R measurements, fostering better predictive models and environmental monitoring.  By integrating AI with GNSS-R, the project seeks to set new standards in the field, focusing on identifying and correcting environmental effects, enhancing the understanding of physical processes, and estimating other crucial geophysical parameters. The exploration of the various AI algorithms is expected to improve the quality of existing data products, particularly under extreme environmental conditions, and broaden GNSS-R's applications in weather forecasting, climate research, and disaster management. AI4GNSSR's innovative efforts are paving the way for enhanced scientific insights and more effective policy development, significantly contributing to global environmental resilience and sustainability.