AI Research Intern (PhD) – 3D Computer Vision

Helsing is a defence AI company. Our mission is to protect our democracies. We aim to achieve technological leadership, so that open societies can continue to make sovereign decisions and control their ethical standards.

As democracies, we believe we have a special responsibility to be thoughtful about the development and deployment of powerful technologies like AI. We take this responsibility seriously.

We are an ambitious and committed team of engineers, AI specialists and customer-facing programme managers. We are looking for mission-driven people to join our European teams – and apply their skills to solve the most complex and impactful problems. We embrace an open and transparent culture that welcomes healthy debates on the use of technology in defence, its benefits, and its ethical implications.

As a PhD research intern in 3D computer vision, you will tackle open research problems in spatial perception and localisation, and explore how novel approaches can push the state of the art.

You will join our Spatial Intelligence Team, which builds systems for scene matching, geo-registration, simultaneous localisation and mapping (SLAM), visual-inertial odometry (VIO), and 3D reconstruction. You will investigate how the latest advances in the research community can add value to deployable systems, prototype the most promising ideas, and evaluate them rigorously under real-world data distributions and compute constraints.

You will work closely with a team distributed across Europe, alongside globally renowned AI experts, while collaborating with a broader team across Europe.

The day-to-day

• Survey and critically assess state-of-the-art research in geometric computer vision, feature matching, place recognition, and SLAM/VIO.

• Conceptualise how emerging techniques could apply to our localisation and reconstruction pipelines, and discuss trade-offs with the team.

• Evaluate the strengths and weaknesses of different algorithmic approaches for concrete, mission-critical use cases.

• Prototype and benchmark promising concepts against real-world operational conditions such as changes in illumination, viewpoint, season, or sensor modality.

• Summarise findings in clear, concise written reports and present them to the team.

You should apply if you

• Are currently pursuing a PhD in computer vision, machine learning, robotics, or a closely related field.

• Have demonstrated theoretical and practical experience in localisation, scene matching, SLAM, visual odometry, or 3D reconstruction.

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