PhD Scholarship: Deep Learning Methods for Characterizing Forest Structure with 3D Point Clouds

The Norwegian University of Life Sciences (NMBU) is offering a 3-year PhD position within the Faculty of Environmental Sciences and Natural Resources Management (MINA), focused on developing deep learning models for 3D forest point clouds. This position is part of the SmartForest research-driven innovation center, which aims to advance digitalized and sustainable forest management using cutting-edge technologies. The successful candidate will work on developing both supervised and self-supervised deep learning algorithms to process and analyze 3D point cloud data from LiDAR and photogrammetry, with the goal of characterizing forest ecosystem structure and function. The project seeks to push the boundaries of current computer vision models, expanding their applicability from high-resolution, small-area datasets to lower-resolution, landscape-level datasets. The candidate will be expected to contribute to a variety of downstream tasks, including the assessment of forest structure and biodiversity. Applicants must have a master's degree in machine learning, computer science, or a forest-related field with a focus on remote sensing, and demonstrate experience with deep learning. Additional skills in handling forest ecosystem data, 3D point clouds, programming (including Docker and containerization), and proficiency in Norwegian or another Scandinavian language are valued. The position offers a starting salary of NOK 550,800 per year and follows national guidelines for PhD scholars in Norway. The application process requires submission of a motivation letter, CV, certified academic documents, proof of English proficiency, references, and other relevant documentation. The faculty is known for its vibrant research culture and international quality, with expertise spanning geology, hydrology, soil science, environmental chemistry, forestry, ecology, and more. NMBU provides a supportive and interdisciplinary environment, with a strong focus on innovation and collaboration. The application deadline is November 10, 2025. For further information, applicants may contact Professors Rasmus Astrup or Terje Gobakken.