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Kungliga Tekniska högskolan, Skolan för elektroteknik och datavetenskap
Third-cycle subject: Electrical engineering
With increasing economic or environmental concerns, advanced ICT technologies have been widely used in the power grid systems, especially when electrical production, selling and storage are more distributed. Thus, it is important to investigate how machine learning or edge computing can be used efficiently in smart grids. In the project, you will work on the research topic of artificial intelligence for smart grids, e.g., nano-grids or multi-layer systems. You will use recent development on machine learning, e.g., neural networks for analysis and prediction gird parameters. You are expected to use real data to predict the demanding or identify outage events in complex grids e.g., those with end-to-end selling or electrical vehicles. You will also establish a test bed for implementing theoretical algorithms. Background/degree on energy systems or/and machine learning is needed. Preferably, you should also know communication networks or edge computing. You will work on an European project with multiple partners.
The Division of Information Science and Engineering consists of faculty members, researchers and doctoral students (ca. 55 people in total) who contribute to a high professional standard of intensive work and quality results, as well as to a friendly and open environment. The staff has a multicultural background and the working language is English. The department is internationally well established, have many research collaborations with excellent partners worldwide, and are involved in several European and national projects. The excellent research environments at the department promote creativity and allow a lot of flexibility so that people can work in a way that fits best to them. The work commitment is very high and the cooperative setting creates a positive sense of belonging.
There are diverse backgrounds in the division, which will give you the opportunity to be trained in the areas of machine learning, communications, and optimization etc.
What we offer
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 an doctoral student at KTH you need to be goal oriented and persevering in your work. In the selection of the applicants, the following will be assessed:
The candidate should have a background/degree on energy systems and/or machine learning. Knowledge in communication networks and edge computing is meriting. Good command of English orally and in writing is required to publish and present results at international conferences and in international journals.
After the qualification requirements, great emphasis will be placed on personal qualities and personal suitability.
Target degree: Doctoral degree
Information regarding admission and employment
Only those who are or have been admitted to third-cycle studies may be employed as a doctoral student. The term of the initial contract may not exceed one year and may thereafter be extended. Doctoral students may engage in teaching, research, and administration corresponding to a maximum of 20 % of a full-time position.
You will find contact information for union representatives on KTH's website .
Doctoral section (Students union on KTH Royal Institute of Technology)
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.
For information about processing of personal data in the recruitment process please read here.
We firmly decline all contact with staffing and recruitment agencies and job ad salespersons.
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 longer than 6 months
Working hours: Full time
Salary: Monthly salary according to kth's doctoral student salary agreement
Reference number: J-2019-1430
Last application date: 2019-07-31
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