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PhD Research Intern – AI & Computer Vision

100% Remote Full-time Open now

We are seeking a PhD Research Intern based in England, UK to join our AI & Robotics Research team for a Summer 2026 internship. This is a remote position offering hands-on experience developing and deploying cutting-edge AI and computer vision research on production robotic systems.

You will work with real-world data collected from a large-scale fleet of autonomous robots operating in complex, dynamic warehouse environments. This role is ideal for PhD students looking to bridge academic research and real-world robotic deployment.

Duration: Summer 2026 (Aprox. 12 weeks) Working hours: 9:00 AM – 5:00 PM (UK local time) Location: Remote (England-based)

Responsibilities

  • Develop advanced computer vision algorithms for object recognition, scene understanding, visual SLAM, and 3D perception in real-world robotic environments.
  • Enhance robotic perception systems supporting navigation, localization, manipulation, and task execution using multimodal sensor data (RGB, depth, LiDAR).
  • Research and prototype modern AI architectures, including:
    • State of the Art Visual SLAM and Structure from Motion
    • Large Language Models (LLMs) for robotics
    • Generative AI and diffusion models
    • Neural rendering, radiance fields, and Gaussian splatting
    • Graph neural networks and self-supervised learning
  • Deploy and evaluate research at scale, transitioning ideas from prototype to production-ready systems.
  • Collaborate closely with researchers and engineers across computer vision, robotics, machine learning, and software engineering.
  • Document and communicate findings through technical reports, internal presentations, and (where appropriate) academic publications.

Qualifications

  • Currently enrolled in a PhD program in Computer Vision, Robotics, Machine Learning, or a closely related field.
  • Strong foundation in AI, deep learning, and computer vision.
  • Hands-on experience with PyTorch (preferred) or TensorFlow.
  • Proficiency in Python; working knowledge of C++ is a plus.
  • Experience with robot perception systems (e.g., RGB-D cameras, stereo vision, LiDAR).
  • Strong analytical and problem-solving skills.
  • Excellent English communication skills and ability to collaborate in a distributed, multidisciplinary team.
  • Experience working with large-scale datasets or real-world robotics deployments.
  • Prior publications in top-tier robotics, CV, or ML venues.
  • Familiarity with SLAM, visual odometry, or 3D reconstruction pipelines.

Why Intern at Locus Robotics?

  • Unmatched real-world data: Work with data from 15,000+ robots operating across hundreds of customer sites globally.
  • Real impact: Your work directly influences production robotic systems used daily in live customer environments.
  • Research meets reality: Bridge cutting-edge academic research with deployment at scale.
  • World-class mentorship: Learn from experienced researchers and engineers working at the intersection of AI and robotics.
  • Career pathways: High-performing interns may be considered for future internships, extended research collaborations, or full-time roles.

Research Areas of Interest

We are particularly interested in candidates excited by one or more of the following:

  • Anomaly Detection
  • Generative AI & Diffusion Models
  • Neural Rendering, Radiance Fields, Gaussian Splatting
  • Robot Localization & Cross-View Geo-Localization
  • Visual SLAM, Visual Odometry, Structure from Motion
  • Monocular Depth Estimation & Semantic Segmentation
  • Birds-Eye View (BEV) Representations
  • Self-Supervised & Representation Learning
  • Reinforcement Learning for Robotics
  • Language Models for Embodied AI

Additional Information

Locus Robotics is an equal opportunity employer

Application Fraud Detection Notice: To help maintain a fair and secure hiring process, Locus Robotics may use AI-assisted and other automated tools to detect suspected fraud, misrepresentation, or misuse of the application process. Hiring decisions are not made solely by automated means unless otherwise disclosed where required by law

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