PhD in AI for Earth Observation
Project description
Third-cycle subject: Geodesy and Geoinformatics
We are excited to announce a new PhD position in Artificial Intelligence for Earth Observation. This research opportunity will focus on advancing AI-driven Earth Observation (EO) methods for environmental intelligence, with applications including illegal waste monitoring, wildfire emission estimation, and pollution assessment.
The doctoral research will explore cutting-edge artificial intelligence and machine learning approaches for analyzing multi-modal, multi-temporal, and multi-resolution satellite imagery and geospatial data. The project aims to develop innovative models capable of capturing complex spatiotemporal dynamics and supporting a broad range of downstream environmental applications. Particular emphasis will be placed on building robust and scalable AI solutions for environmental monitoring, situational awareness, and sustainable decision-making through EO big data analytics.
If you are passionate about applying artificial intelligence to address environmental challenges and have a strong background in machine learning, computer vision, or remote sensing, we encourage you to apply and join us in shaping the future of AI-driven Earth observation for environmental intelligence.
Supervision: Yifang Ban is proposed to supervise the doctoral student. Decisions are made on admission
Admission requirements
To be admitted to postgraduate education (Chapter 7, 39 § Swedish Higher Education Ordinance), the applicant must have basic eligibility in accordance with either of the following:
passed a second cycle degree (for example a master's degree), or
completed course requirements of at least 240 higher education credits, of which at least 60 second-cycle higher education credits, or
acquired, in some other way within or outside the country, substantially equivalent knowledge
Master's Degree in Geomatics, Computer Science, Electrical Engineering, or related disciplines in natural sciences and engineering.
Strong proficiency in image analysis, computer vision, pattern recognition, machine learning/deep learning, along with solid background in data science.
Prior experience with generative AI and/or geospatial foundation models is a valuable asset.
Proficient coding skills in widely used scientific programming languages, including Python, C++, and Matlab.
Excellent ability to read and write scientific English and fluent spoken English.
In addition to the above, there is also a mandatory requirement for English equivalent to English B/6.
Selection
In order to succeed as a doctoral student at KTH you need to be goal oriented and persevering in your work. During the selection process, candidates will be assessed upon their ability to:
independently pursue his or her work
collaborate with others,
have a professional approach and
analyze and work with complex issues.
After the qualification requirements, great emphasis will be placed on personal skills.
