Locus Robotics is a global leader in warehouse automation, delivering unmatched flexibility and unlimited throughput, and actionable intelligence to optimize operations. Powered by LocusONE, an AI-driven platform, our advanced autonomous mobile robots seamlessly integrate into existing warehouse environments to enhance efficiency, reduce costs, and scale operations with ease. Trusted by over 150 industry leading retail, healthcare, 3PL, and industrial brands in over 350 sites worldwide, Locus enables warehouse operators to achieve rapid ROI, minimize labor costs, and continuously improve productivity. Our industry-first Robots-as-a-Service (RaaS) model ensures ongoing innovation, scalability, and cost-effectiveness without the burden of significant capital investments. With proven capabilities in diverse workflows—from picking and replenishment to sorting and pack-out—Locus Robotics empowers businesses to meet peak demands and adapt to ever-changing operational needs.

We are seeking a PhD student based in England, UK for a remote internship focused on cutting-edge AI and Computer Vision research. This role offers the opportunity to develop and deploy novel AI architectures at scale, leveraging real-world data from our fleet of over 13,000 autonomous robots. You will work on advanced topics such as Computer Vision, Large Language Models, Graph Neural Networks, and Neural Rendering, gaining hands-on experience in applying AI to real-world challenges.

Responsibilities

• Develop Cutting-Edge Computer Vision Algorithms: Develop advanced computer vision techniques for object recognition, scene understanding, visual SLAM, and more to improve the performance and autonomy of mobile robots in complex, dynamic environments.
• Enhance Perception Systems: Collaborate with a cross-functional team to enhance robot perception for navigation, grasping, and task execution in real-world environments.
• Develop Generative AI and Language Models: Work on advanced LLM, GenAI, Language-Enabled Radiance Fields using our industry leading robotics data.
• Research and Innovation: Drive innovative research in deep learning, 3D computer vision, sensor fusion, and robotics perception.
• Deploy Solutions: Implement and test your research on real-world systems in a fast-paced, production-oriented environment.
• Collaborate: Work closely with experts in computer vision, robotics, software engineering, and machine learning to tackle real-world challenges in robotics.

Qualifications

• Currently enrolled in a PhD in Computer Vision, Robotics, Machine Learning, or a related field.
• Strong background in AI, Computer Vision and Deep Learning, including experience with frameworks like PyTorch (preferred), TensorFlow, OpenCV, Open3D, etc.
• Proficiency in Python and C++ programming languages
• Hands-on experience with robot perception systems (e.g., LiDAR, RGB-D cameras, stereo vision, etc.)
• Strong research and problem-solving skills, with a passion for tackling real-world challenges in robotics.
• Excellent English communication skills and ability to work collaboratively in a multidisciplinary team.

Why have your internship at Locus Robotics?

• Industry Leading Data: With over 13,000 robots operating in the field, over 200 sites and more than 100 customers, Locus Robotics offers unparalleled access to data to tackle AI-related problems.
• Cutting-Edge Technology: Work with the latest in autonomous mobile robotics and AI.
• Innovative Work Environment: Be part of a company that fosters creativity, collaboration, and continuous learning.
• Real-World Impact: Contribute directly to the development of solutions that are changing the way warehouses and distribution centers operate.
• Mentorship: Gain invaluable experience working with top-tier researchers and engineers in the field.
• Career Growth: Opportunities for continued employment or collaboration after the internship, depending on performance and business needs.

Areas of Interest

• Anomaly Detection, Generative AI, Radiance Fields, Gaussian Splatting, Robot Localization, Cross-View Geo-Localization, Language Models, Monocular Depth Estimation, Semantic Segmentation, Birds-Eye View, SLAM, Visual Odometry, Structure from Motion, Self-Supervised Learning, Reinforcement Learning, Diffusion Models