PhD in Applied Mathematics / Computer Vision / Machine Learning / 3D Vision at Lund University Lund University in Sweden

PhD opportunities are available at Lund University in Applied Mathematics with research directions in computer vision, machine learning, 3D reconstruction, 3D vision, geometry, spatial AI, and medical imaging.

The main research environment is the Division of Computer Vision and Machine Learning at the Centre for Mathematical Sciences, affiliated with both the Faculty of Engineering (LTH) and the Faculty of Science. The group works on geometry and computer vision, medical image analysis, and machine learning/artificial intelligence, with projects involving 3D reconstruction, navigation from images and video, structure-from-motion, sensor data, acoustic data, and multimodal medical image analysis.

One opening focuses on methods for three-dimensional reconstruction and navigation from images and video, combining geometric computer vision with modern machine learning and physically informed neural networks. Another opening focuses on computer vision and machine learning for audio-based 3D mapping / spatial AI, including end-to-end methods for 3D estimation from sensor data. A third opening focuses on machine learning for medical imaging, including hybrid CNN-transformer architectures, attention mechanisms, multimodal learning, and integration of imaging and patient data.

The positions are doctoral student openings, meaning the successful candidate is both admitted as a student and employed by Lund University. The employment is fixed-term, full-time, and normally lasts 4 years, with possible extension for teaching and departmental duties (up to 20%). The positions are funded by ELLIIT and linked to projects such as Learning Geometric Representations and Next Generation Spatial AI.

Eligibility highlights include a second-cycle degree or equivalent, at least 90 credits relevant to the subject area (with at least 45 at second-cycle level), strong independence, good collaboration skills, excellent English, and relevant programming/machine-learning experience. Preferred skills include Python or C++, PyTorch/TensorFlow, and prior work in computer vision, deep learning, or medical image analysis.

Application materials typically include a CV, cover letter, degree certificates, study transcripts, and optional references or recommendation letters. Applications are in English and submitted via the university application portal. Deadlines mentioned in the post are 15 May 2026 for the linked openings.