Research Assistant I, S1

The Research Assistant I will support the research, development, and testing of autonomous robotic systems and AI models through simulation and real-world integration. The student will assist in developing control logic, training intelligent agents, and debugging software in environments such as Gazebo. This role offers hands-on experience in the intersection of AI development, robotics simulation, and applied autonomy.

Duties/Responsibilities
• Perform research and develop, simulate, and test autonomous behaviors and autonomous control algorithms in Gazebo. This includes creating simulation environment, developing AI models, and developing mission scenarios.
• Integrate software systems into real hardware system.
• Perform troubleshooting and debugging across simulation and physical systems. This involves identifying communication issues, simulation instability, and deployment errors.
• Assist in hardware-in-the-loop (HIL) and software-in-the-loop (SIL) testing, connecting virtual environments to physical components to validate control logic.
• Collaborate with cross-disciplinary team members (e.g., AI, engineer, UX/UI) to refine software requirements and ensure integration success.
• Document work progress, codebases, testing procedures, and system configurations for reproducibility and knowledge sharing.

• College coursework preferably in Science, or Engineering with moderate technical knowledge.
• Familiarity with robotics simulation environments (e.g., Gazebo, Webots).
• Basic understanding of AI concepts such as supervised learning, decision trees, or reinforcement learning.
• Strong problem-solving skills and the ability to work both independently and collaboratively.
• Strong communication and documentation skills.

• Current enrollment in a Bachelor’s or Master’s program in Computer Science, Artificial Intelligence, Robotics, Engineering, or a related technical field preferred.
• Proficiency in Python and/or C++ programming.
• Experience with ROS (Robot Operating System) and robotic control software.
• Hands-on experience with machine learning frameworks (e.g., TensorFlow, PyTorch).
• Exposure to agent-based modeling, reinforcement learning, or behavioral AI systems.
• Familiarity with hardware-in-the-loop (HIL) or software-in-the-loop (SIL) testing methods.
• Comfortable working in Linux environments and using tools like Git for version control.
• Interest in applying AI and robotics to real-world mission-critical systems.

$35,776