Doctoral Student in Urban Heat Prediction Using AI and Earth Observation Full-time NEW

Doctoral Student in Urban Heat Prediction Using AI and Earth Observation

KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science

Project description: Third-cycle subject: Computer Science

The Division of Robotics, Perception and Learning has an open position for a doctoral student with a background and strong interest in deep generative learning and computer vision/remote sensing. The successful candidate will join a project funded by Digital Futures to map urban air temperature at high spatial and temporal resolutions. Cities face rising urban heat, both from sharp spikes and prolonged hot periods; however, producing these high-resolution urban air temperature maps remains an open problem. This project will treat dense urban air temperature estimation as an inverse problem, using conditional diffusion models to fuse satellite, forecast, and crowd-sourced station data into probabilistic maps. The challenge: conditioning generative models on large, heterogeneous, incomplete data will require the student to work on cutting-edge ML for a pressing societal need.

You will be part of an ongoing collaboration between the groups of Assoc. Prof. Josephine Sullivan at RPL, Prof. Yifang Ban (Division of Geoinformatics) at the Department of Urban Planning and Environment, and Sebastian Hafner at RISE, Kista.

Supervision: Associate Professor Josephine Sullivan is proposed to supervise the doctoral student.

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.
• This project will require practical proficiency in deep learning. Thus demonstrated competency in deep learning programming libraries such as TensorFlow, PyTorch, or JAX is a must and experience with GPU-based experimentation and cluster computing (e.g., Docker, Slurm) a plus.
• 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.

In the evaluation of candidates, an emphasis will be place on academic results, completed courses and demonstrated programming ability via completed project work. An earlier specialization in machine learning and experience with computer vision and/or remote sensing is highly desirable and especially meritorious.

After the qualification requirements, great emphasis will be placed on personal skills.

Target degree: Doctoral degree

Information regarding admission and employment: Only those admitted to postgraduate education may be employed as a doctoral student. The total length of employment may not be longer than what corresponds to full-time doctoral education in four years' time. An employed doctoral student can, to a limited extent (maximum 20%), perform certain tasks within their role, e.g. training and administration. A new position as a doctoral student is for a maximum of one year, and then the employment may be renewed for a maximum of two years at a time. In the case of studies that are to be completed with a licentiate degree, the total period of employment may not be longer than what corresponds to full-time doctoral education for two years.

As a doctoral student, you are entitled to a workplace with many employee benefits and monthly salary according to KTH's Doctoral student salary agreement . Read more about Doctoral studies (PhD) | KTH | Sweden.