Intern - FCS

About the Role:

Key Responsibilities:

• Flight Dynamics and Control: Assist in the design, analysis, and tuning of control algorithms (e.g., PID, LQR, model predictive control) for stable and responsive flight control, considering flight dynamics principles.
• ML/AI Integration: Support the use of machine learning models and AI techniques for adaptive control, predictive maintenance, fault detection, and system optimization.
• Vision-Based Control: Develop and implement vision-based control strategies using computer vision and image processing algorithms for navigation, landing, obstacle avoidance, GPS denied navigation and target tracking.
• Flight Simulation: Conduct simulations of flight dynamics and control systems using MATLAB/Simulink or similar tools, incorporating both traditional control methods and ML/AI-enhanced approaches.
• Sensor Fusion & Data Integration: Combine data from various sensors (e.g., cameras, LiDAR, IMUs) to enhance flight control systems and improve vehicle performance in complex environments.
• Data Analysis: Analyze flight and sensor data to tune control parameters, using machine learning for anomaly detection, pattern recognition, and performance enhancement.
• Embedded Systems: Work on the implementation of control algorithms in embedded systems, including the deployment of AI/ML models for real-time control and decision-making.
• Research and Development: Conduct research on advanced guidance, navigation, and control (GNC) strategies using flight dynamics, AI/ML, and vision-based technologies to enhance the autonomy of aerospace vehicles.
• Documentation: Prepare technical reports and document flight control designs, test plans, and simulation results for review.

Required Qualifications:

• Currently pursuing a Bachelor’s or Master’s degree in Aerospace Engineering, Mechanical Engineering, Electrical Engineering, Computer Science, or a related field.
• Strong interest and understanding of flight dynamics, control theory, and vehicle aerodynamics.
• Experience with MATLAB/Simulink for modeling and simulation of dynamic systems and control design.
• Basic knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) and computer vision (OpenCV).
• Familiarity with programming languages such as Python, C/C++ for control algorithm development and AI/ML model implementation.
• Interest in UAVs, aircraft systems, and/or spacecraft control systems.
• Analytical problem-solving skills with attention to detail.

Preferred Qualifications:

• Experience with aerospace control systems such as PX4, ArduPilot.
• Knowledge of flight dynamics modeling and simulation tools such as X-Plane, Gazebo, or FlightGear.
• Familiarity with vision-based navigation, SLAM (Simultaneous Localization and Mapping), and sensor fusion techniques.
• Experience with hardware-in-the-loop (HIL) testing, real-time simulations, or flight testing environments.
• Understanding of AI techniques like reinforcement learning, neural networks, or decision-making algorithms for control.
• Knowledge of ROS (Robot Operating System) for integrating control systems and AI/ML algorithms.
• Experience with real-time operating systems (RTOS) and embedded software development.

What You Will Gain:

• Hands-on experience in flight dynamics, control system design, and integration of ML/AI technologies in aerospace applications.
• Exposure to state-of-the-art simulation tools, control systems, and autonomous flight technologies.
• Collaboration with professionals in GNC, AI, and vision-based system development, gaining insights into both classical and modern control methods.
• Practical experience in hardware-in-the-loop testing, sensor fusion, and flight control optimization.
• Networking opportunities in the aerospace and AI industries, with potential pathways for future career growth.