Intern - FCS

NewSpace Research and Technologies; Bengaluru; On-site; INR 1,436,781 - 2,394,636; Full time

Benefits offered by this job

Hands-on experience

Networking opportunities

Exposure to advanced technologies

Job summary

NewSpace Research and Technologies in Bengaluru is seeking a motivated Aerospace Control Systems Intern. The role involves assisting in the design and development of control algorithms for UAVs, integrating advanced autonomy technologies using AI and machine learning.

Candidates should be pursuing a degree in relevant engineering fields, with strong interests in flight dynamics and control systems. This internship provides hands-on experience with cutting-edge technologies and potential career growth in aerospace industries.

Qualifications

• Currently pursuing a degree in a relevant field.
• Understanding of flight dynamics and control theory.
• Experience with MATLAB/Simulink for dynamic systems.

Responsibilities

• Assist in design and tuning of control algorithms.
• Support ML models for adaptive control and predictive maintenance.
• Develop vision-based control strategies for UAVs.

Skills

Flight dynamics

Control theory

Machine learning

Computer vision

MATLAB/Simulink

Python

C/C++ programming

Data analysis

Education

Bachelor’s or Master’s degree in Aerospace Engineering, Mechanical Engineering, Electrical Engineering, Computer Science

Tools

MATLAB

Simulink

TensorFlow

OpenCV

Job description

We are looking for a motivated Aerospace Control Systems Intern with a strong interest in flight dynamics, control systems, and emerging technologies such as machine learning (ML), artificial intelligence (AI), and vision-based control. In this role, you will be part of a multidisciplinary team developing cutting‑edge control systems for UAVs, integrating them with advanced autonomy technologies to improve the performance and decision‑making capabilities of aerospace vehicles.

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.