Clara Miranda García

• Worked on S2VSE, a Galileo-related GNSS simulation environment, profiling long-running Python workflows to identify CPU-bound bottlenecks and inefficient legacy code sections. • Designed a process-based execution module for server-side parallel simulation runs, addressing Python GIL limitations, serialization overhead and Windows multiprocessing constraints. • Refactored simulation modules to improve runtime performance, maintainability and testability, significantly reducing runtime in selected workf

• Developed Python components for Microsoft Graph and Teams data ingestion, ensuring efficient OAuth2 authentication. • Implemented Celery for asynchronous data processing, enhancing the performance of Django applications. • Cleaned and normalized API data for reliable SQL storage, facilitating accurate downstream analysis. • Contributed to the development of Django backend components, including models, migrations, and validation logic.

Completed a year abroad at the Technical University of Munich (TUM), with coursework focused on machine learning, natural language processing, computational biology, and blockchain-based systems.