
Yasmine
Python Developer and AI Machine Learning Specialist
Competenze

Consulta i miei servizi

Esperienza lavorativa
Embedded Systems & AI Developer
IEEE AESS & IES Challenge • Part time
Nov 2025 - Dec 2025 • 1 mo
Designed and implemented an intelligent embedded monitoring system for the CubeSat Electrical Power System (EPS) as part of the IEEE AESS & IES Challenge 2025. Main contributions: • Development of a two-layer architecture integrating MCU and On-Board Computer (OBC) • Implementation of lightweight AI models (Autoencoder and LSTM) for fault detection and anomaly prediction • Real-time communication between EPS32 microcontroller and the OBC • Data visualization for onboard monitoring and system diagnostics Technologies used: Python, C, embedded systems programming, machine learning models, and real-time data communication.
Mobile Health Application Developer
EL RIADH CLINIC • Full time
Aug 2025 - Sep 2025 • 1 mo
Developed ClinLink, a mobile application designed to facilitate international patient management and coordination in collaboration with El Riadh Clinic in Sfax. Key features: • Real-time geolocation and navigation services • Appointment and accommodation scheduling for international patients • Secure messaging system between patients and medical staff • Push notifications for medical updates and reminders • AI-based assistant chatbot for patient support Technologies used: Flutter, APIs integration, mobile system architecture, and secure communication protocols.
Data Analyst – Climate & Wildfire Data Project
ISIMS
Jun 2025 - Jul 2025 • 1 mo
Designed and developed a complete data analysis pipeline to study the evolution of forest fires in France (Nouvelle-Aquitaine and PACA regions). Main tasks: • Integration of wildfire datasets with meteorological data (Météo-France and ERA5) • Data extraction, preprocessing, and geospatial alignment • Identification of climate patterns influencing wildfire occurrence • Development of interactive visualizations including heatmaps and time-series analysis • Contribution to predictive modeling research for wildfire risk prevention Technologies used: Python, Pandas, Jupyter Notebook, data visualization, geospatial analysis.