Master Thesis - Benchmarking AI on Resource-Constrained Micro Drones
Core
Design and validate a benchmarking framework for AI models deployed on resource-constrained micro-drones, measuring performance, resource usage, and energy trade-offs.
Role type
Master Thesis Researcher (Embedded AI & Robotics)
Builds
A repeatable benchmarking framework and a documented evaluation method for micro-drone platforms.
Domain
Robotics, Embedded Systems, Artificial Intelligence
Deliverable
production ML models
Required skills
Python, C++, Machine Learning, Control Systems, Simulation environments
Preferred skills
Data Science, Embedded deployment, Autonomous systems
Technologies
Python, C++, Simulation assets, Micro-drone hardware
Responsibilities
Define benchmark metrics and KPIs for AI model performance; Adapt deployment pipelines for ML inference on micro-drones; Develop measurement, logging, and analysis workflows; Execute test suites on simulation and real hardware.
Seniority
Master Thesis Student (30 credits, 20 weeks)
