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Third-cycle subject: Sustainable buildings
In this project, machine learning based simulation tools will be developed to both predict the air distribution and inverse track pollutants in a city level. Global urbanization and climate change have urged the need of transforming urbans, from small to large, to healthier and smarter cities. One of the largest challenges lies in understanding the pollutant transporting phenomena in the city and finding out the sources of such pollutants to mitigate. Cities are complex bodies, pollutants spread in a highly interdependent and interactive approach, which are highly depended on the urban planning, traffic and transportation status, energy systems as well as building designs. As a result, a tool that can support the decision making and assist mitigating the pollutant resources are crucial. However, the development of such tools is commonly pledged by long computing time, low accuracy, complicated models as well as case-to-case solutions. For urban planners, environmental engineers and public owners, efficient and holistic understanding of the consequences and spreading mechanism are in need in order to provide decision supports and pollutant source control strategies in a large scale. The purpose of the project is to contribute to the accelerations of future smart and healthy cities designs. This project has a strong focus on urban airflow predictions, pollutant predictions, and source tracking by developing advanced simulation tool in large city scale
Supervision: The doctoral student will be supervised by: assistant professor Wei Liu and professor Folke Björk
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:
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:
After the qualification requirements, great emphasis will be placed on personal qualities and personal suitability.
It is also important that the applicant has experience and knowledge in computational fluid dynamics, in machine learning and in C++/python programming. Experience in teaching assistant is a plus.
Target degree: Licentiate degree
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.
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You will find contact information for doctoral section on the section's website.
Apply for the position and admission through KTH's recruitment system. It is the applicant’s responsibility to ensure that the application is complete in accordance with the instructions in the advertisement.
Applications must be received at the last closing date at midnight, CET/CEST (Central European Time/entral European Summer Time).
Applications must include the following elements:
Gender equality, diversity and zero tolerance against discrimination and harassment are important aspects of KTH's work with quality as well as core values in our organization.
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Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.
Type of employment | Temporary position |
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Contract type | Full time |
First day of employment | According agreement |
Salary | Monthly salary according to KTH's doctoral student salary agreement |
Number of positions | 1 |
Full-time equivalent | 100% |
City | Stockholm |
County | Stockholms län |
Country | Sweden |
Reference number | A-2020-1999 |
Contact |
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Published | 08.Oct.2020 |
Last application date | 05.Nov.2020 |