Doctoral Student in Urban Heat Prediction Using AI and Earth Observation KTH Royal Institute of Technology in Sweden

Doctoral Student in Urban Heat Prediction Using AI and Earth Observation at KTH Royal Institute of Technology in Stockholm, Sweden, is an open PhD position in the third-cycle subject Computer Science.

The project sits in the Division of Robotics, Perception and Learning and is focused on a timely and socially important problem: mapping urban air temperature at high spatial and temporal resolution. Cities are increasingly affected by urban heat, including short spikes and longer heat periods, but dense high-resolution temperature mapping remains challenging. The research will explore the use of conditional diffusion models and other deep generative learning approaches to fuse heterogeneous data sources such as satellite observations, forecast data, and crowd-sourced station measurements into probabilistic urban temperature maps. This is a cutting-edge machine learning problem with clear relevance to climate resilience, urban environments, and earth observation.

The doctoral student will join an active research collaboration involving Associate Professor Josephine Sullivan at RPL, Professor Yifang Ban from the Division of Geoinformatics at the Department of Urban Planning and Environment, and Sebastian Hafner at RISE in Kista. Supervision is proposed by Associate Professor Josephine Sullivan.

Applicants should have strong preparation for postgraduate study, including a relevant second-cycle degree or equivalent academic background. The position requires practical proficiency in deep learning and demonstrated programming ability, particularly with libraries such as TensorFlow, PyTorch, or JAX. Experience with GPU-based experimentation and cluster computing tools such as Docker and Slurm is advantageous. English proficiency equivalent to English B/6 is mandatory. Prior experience in machine learning, computer vision, and/or remote sensing is highly desirable.

Funding is provided through Digital Futures, and the doctoral student receives a monthly salary according to KTH’s doctoral student salary agreement. The appointment is a full-time temporary doctoral position. Applications must be submitted through KTH’s recruitment system by 2026-09-24.

Application materials include diplomas and transcripts, evidence of language qualifications, a CV, a motivation letter describing research interests and goals, and representative publications or technical reports. KTH asks applicants to ensure that submissions are complete and follows the stated instructions carefully.

This position is a strong fit for candidates interested in AI for environmental sensing, remote sensing, computer vision, urban climate modeling, and probabilistic machine learning within a leading technical university environment in Sweden.