About the Role
We're seeking a Research Intern passionate about applying AI and machine learning to autonomous UAV perception and navigation. You'll explore and prototype learning-based algorithms that help drones see, localize, and move intelligently even in dynamic or GPS-denied environments.
This internship provides hands-on exposure to simulation environments, sensor-fusion pipelines, and embedded AI systems.
Key Responsibilities:
- Support research and prototyping of AI/ML algorithms for perception, tracking, localization and navigation.
- Work on multi-sensor fusion combining visual, inertial, and positional data for improved environmental understanding.
- Train and test computer vision or deep learning models for detection, motion estimation, and environmental understanding.
- Use high-fidelity simulations with physics-based environments to generate datasets and test autonomy pipelines.
- Assist in integrating perception and navigation modules into flight-control or hardware-in-the-loop setups.
- Optimize real-time inference and video/data streaming pipelines for embedded AI systems.
Skills & Qualifications:
- Strong foundation in deep learning, computer vision, and state estimation.
- Understanding of object detection, tracking, SLAM, and sensor-fusion techniques.
- Proficiency in Python and familiarity with PyTorch/TensorFlow and OpenCV.
- Experience working on Linux environments and basic knowledge of C++.
- Familiarity with simulation tools (Gazebo, Webots,Isaac Sim) and autopilot frameworks (PX4, ArduPilot).
What you'll Gain:
- End-to-end exposure to AI-driven autonomous navigation and perception systems.
- Experience with simulation-driven development and testing of UAV autonomy algorithms.
- Practical exposure to real-time AI model deployment on embedded hardware.
- Opportunity to contribute to next-generation UAV guidance and autonomy research
