We invite applications from talented and highly motivated candidates to pursue a PhD degree in machine learning at KTH Royal Institute of Technology in Stockholm, Sweden. This position is funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP), Sweden's largest individual research program.
Official Job Advertisement: https://www.kth.se/lediga-jobb/898611?l=en
Project Description
The successful candidate will be supervised by Professor Aristides Gionis at the School of Electrical Engineering and Computer Science, KTH. The research team focuses on developing novel methods to extract knowledge from data, modeling large-scale complex systems, and exploring new application areas in data science.
Research areas of interest include, but are not limited to:
Models and algorithms for knowledge discovery
Novel algorithmic and statistical techniques for big data management
Optimization for machine learning
Analysis of information and social networks
Fairness, accountability, and transparency in learning systems
About the WASP Program
The advertised position is funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP), Sweden's largest individual research program. WASP is a major national initiative for strategically motivated basic research, education, and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information, and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems, and software for the benefit of Swedish industry.
Admission Requirements
To be admitted to postgraduate education, the applicant must have:
A passed second-cycle degree (e.g., a master's degree), or
Completed course requirements of at least 240 higher education credits, of which at least 60 are second-cycle credits, or
Acquired substantially equivalent knowledge in some other way
Proficiency in English equivalent to English B/6
Selection Criteria
During the selection process, candidates will be assessed on their ability to:
Independently pursue their work
Collaborate with others
Maintain a professional approach
Analyze and work with complex issues
Demonstrate strong academic credentials
After qualification requirements, great emphasis will be placed on personal skills.
Employment Conditions
The doctoral student will be employed for the duration of the doctoral education, which corresponds to full-time study for four years. Employment may be renewed for a maximum of two years at a time. A doctoral student may perform limited duties (maximum 20%) such as teaching and administration. The position comes with a monthly salary according to KTH's doctoral student salary agreement and includes employee benefits.
Important Dates
Application Deadline: March 5, 2026 (midnight CET/CEST)
Employment Start: According to agreement
For questions about the position, please contact:
Professor Aristides Gionis: argioni@kth.se
For questions about the recruitment process, please contact:
HR, Anna Olanås Jansson: annaoj@kth.se
Union Representatives
Contact information for union representatives is available on the KTH website.
Doctoral Students' Network
Contact information for the doctoral section at KTH is available on the KTH website.
The position may be classified as security-sensitive. If applicable, a security clearance will be conducted with the applicant's consent.
KTH values equality, diversity, and equal opportunities as integral to its core values.
In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.
We look forward to receiving your application.
Applicants must hold a Master of Science degree by the time of enrollment, in computer science, machine learning, AI, data science, or a related area. Successful candidates should be highly self-motivated and committed to publishing and presenting high-quality research. A solid background in algorithms design, machine learning, and optimization is essential, along with strong programming and implementation skills. Applicants must have strong academic credentials, demonstrated by excellence in coursework or relevant projects.
Application Deadline: March 5, 2026 (midnight CET/CEST)
