School of Engineering - Project Staff (Robotics and Unmanned Systems)

1. Physical & Embodied AI Development
•    System Design & Prototyping: Design, develop, and prototype intelligent physical systems, robotics, and advanced automation platforms leveraging ROS2.0.
•    Hardware-Software Integration: Integration of advanced AI models, computer vision, and operational systems with sensors, actuators, edge devices, and embedded systems.
•    Applied Research & Piloting: Drive feasibility studies and pilot programs involving AI-enabled automation, unmanned systems (UAVs/AGVs), and smart monitoring solutions.
•    End-to-End Project Support: Manage the full lifecycle of applied research and proof-of-concept initiatives, including data preparation, rigorous experimentation, performance evaluation, comprehensive documentation, and post-deployment technical support.
•    Prototype Commercialization: Assist in translating high-potential AI prototypes into scalable, deployable, or licensable industry solutions.
2. Knowledge Sharing & Ecosystem Enablement
•    Educational Mentorship: Support curriculum development and actively mentor student capstone and research projects in robotics and applied AI domains.
•    Stakeholder Collaboration: Partner closely with internal researchers and external industry stakeholders on collaborative innovation initiatives.
•    Technology Advocacy: Lead and support engaging AI and robotics demonstrations for lab visits, innovation showcases, and executive partner engagements.
•    Use Case Curation: Synthesize and curate compelling physical AI use cases to engage, educate, and inspire both academic and industrial audiences.

•    Qualification in Robotics Engineering, Computer Science, Electrical/Electronic Engineering, Mechatronics, or a closely related quantitative field.
•    Robotics Architecture: Proven hands-on experience developing with ROS2 (Robot Operating System). Familiarity with robotic arms and humanoid robots, robot kinematics, dynamics, and simulation tools (e.g., Gazebo, Isaac Sim) is highly valued.
•    AI & Computer Vision Integration: Practical experience deploying Machine Learning/Deep Learning models (e.g., PyTorch, TensorFlow) and computer vision frameworks (e.g., OpenCV) onto physical platforms.
•    Embedded Software Engineering: Proficiency in C++ and Python, with strong software development best practices (Git, clean architecture, code reviews).
•    Hardware & Edge Systems: Experience working with microcontrollers and single-board computers (e.g., NVIDIA Jetson, Raspberry Pi). Solid understanding of hardware communication protocols (I2C, SPI, UART, CAN bus) and sensor fusion (LiDAR, IMUs, cameras).

Soft Skills & Core Attributes
•    Collaborative Mindset: Exceptional interpersonal skills with a proven ability to collaborate effectively across diverse teams of engineers, researchers, students, and corporate partners.
•    Communication & Presentation: Confident and articulate presenter capable of breaking down complex technical concepts into accessible demonstrations for non-technical audiences.
•    Passion in Educational Mentorship: Guiding student learning journeys and contributing to educational frameworks.
•    Adaptability: A thriving curiosity for ambiguous, fast-paced R&D environments where troubleshooting physical hardware requires creative problem-solving.