Autonomous Machines - ARC

Autonomous Machines - ARC

Embodied AI and Autonomous Agents, Semantic Mapping and Scene Understanding, 3D Computer Vision and Point Cloud Processing, Grasp Planning and Manipulation, Multi-Robot Coordination and Swarm Robotics, Model Predictive Control (MPC), Autonomous Exploration and Frontier Planning, Navigation using Vision Foundation Models (CLIP, DINOv2, SigLIP), Digital Twins and Sim-to-Real Transfer, Safety-Critical Autonomous Systems, Edge AI deployment on NVIDIA Jetson platforms, Warehouse Automation, Industrial Robotics, and Field Robotics Essential Qualification: Master's in Robotics, Computer Science, AI, Machine Learning, Electrical Engineering, or related field. Desired Qualification : PhD in robotics field Publications in Tier-1 conferences and Q1 journals, patents, or significant project contributions in robotics, autonomous systems, embodied AI, or computer vision. Experience deploying autonomous systems on real-world robotic platforms. Design and develop Simultaneous Localization and Mapping (SLAM) algorithms for robust localization and mapping in indoor and outdoor environments. Research, implement, and evaluate Vision-Language Navigation (VLN) models for instruction-guided autonomous navigation. Develop and deploy Vision-Language-Action (VLA) models enabling robots to perceive, reason, and execute complex manipulation tasks. Build autonomous navigation solutions using path planning, motion planning, obstacle avoidance, and trajectory optimization techniques. Work with robotic platforms including wheeled robots, UGVs, UAVs, mobile manipulators, and drone-based manipulation systems. Develop kinematic and dynamic models, including forward kinematics, inverse kinematics, motion control, and whole-body planning for robotic manipulators. Integrate and optimize robotic software using ROS/ROS 2, sensor fusion frameworks, and distributed robotic architectures. Create high-fidelity simulation environments using Isaac Sim, Gazebo, Omniverse, AirSim, MuJoCo, or similar simulators for development and validation. Implement perception pipelines leveraging computer vision, visual foundation models, multimodal AI, and sensor fusion across cameras, LiDARs, IMUs, GPS, and depth sensors. Utilize the NVIDIA Physical AI ecosystem, including Isaac Lab, Isaac Sim, Isaac ROS, Cosmos, Jetson, and Omniverse for robot learning and deployment. Develop AI-driven robotic solutions using Python, deep learning frameworks (PyTorch/TensorFlow), and reinforcement learning techniques. Evaluate robotic systems using simulation and real-world benchmarks, focusing on robustness, safety, scalability, and deployment readiness.