KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as architecture, industrial management, urban planning, history and philosophy.
Third-cycle subject: Electrical engineering
The research involves mathematical and algorithmic modeling and analysis, as well as system design and experimentation. Expertise in the following areas is required: statistics, machine learning, security, networking, and programming. The candidate must hold a master degree in computer science, electrical engineering, or a related area.
The following research direction will be investigated: A hard problem in system security stems from the fact that both attack methods (due to increasing sophistication) and target systems (due to functional and other upgrades) constantly evolve. As a consequence, defense mechanisms must dynamically adapt to remain effective. The project will investigate various approaches for self-learning systems that will address this problem. The research will follow an adversarial approach, based on reinforcement learning, genetic algorithms, or game theory, whereby the strategies of simulated attackers and defenders co-evolve. The target technology on which concepts will be implemented and evaluated is networked systems, in particular 5G networks and their control systems.
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 must have expertise in the following areas: statistics, machine learning, security, networking, and programming as well as hold a master degree in computer science, electrical engineering, or a related area. The candidate should have very good skills in oral and written communication.
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.
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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:
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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 longer than 6 months
|Intitulé||Doctoral student in Self-learning Systems for Network Security|
|Employer||KTH Royal Institute of Technology|
|Job location||Valhallavägen 79, 100 44 Stockholm|
|Publié||juin 13, 2019|
|Date limite d'inscription||août 8, 2019|
|Types d'emploi||PhD  |
|Domaines de recherche :||Intelligence artificielle,   Réseau de neurones artificiels,   Informatique et société,   Communications informatiques (réseaux),   Cyber Security,   Apprentissage automatique,   Vision industrielle,   Vision par ordinateur  |