Doctoral Student (Licentiate) in Artificial Intelligence for Earth Observation at KTH Royal Institute of Technology
KTH Royal Institute of Technology is recruiting a
Doctoral Student (Licentiate)
in
Artificial Intelligence for Earth Observation (AI4EO)
within the Division of Geoinformatics at the School of Architecture and Built Environment.
This PhD-level research opportunity focuses on applying
artificial intelligence, machine learning, computer vision, remote sensing, geoinformatics, and geospatial data analytics
to environmental intelligence. The project will work with multi-modal, multi-temporal, and multi-resolution satellite imagery and geospatial data to develop robust and scalable AI methods for applications such as illegal waste monitoring, wildfire emission estimation, pollution assessment, environmental monitoring, situational awareness, and sustainable decision-making.
The proposed supervisor is
Yifang Ban
, Professor and Director of Division at KTH Royal Institute of Technology. The position is based in
Stockholm, Sweden
.
Eligibility highlights:
applicants should have a second-cycle degree or equivalent, preferably a master's degree in Geomatics, Computer Science, Electrical Engineering, or a related natural science/engineering field. Strong skills in image analysis, computer vision, pattern recognition, machine learning/deep learning, and data science are expected. Experience with generative AI and/or geospatial foundation models is an advantage. Programming ability in Python, C++, and Matlab is required, along with excellent scientific English and fluent spoken English. English proficiency equivalent to English B/6 is mandatory.
Funding and employment:
this is an employed doctoral student position with monthly salary according to KTH’s doctoral student salary agreement. The role is full-time and temporary; for licentiate studies, the employment period may not exceed two years.
Application window:
applications close on
9 July 2026
. Applicants must apply through KTH’s recruitment system and include diplomas, grades, proof of language requirements, CV, motivation letter, and representative publications or technical reports.
