Sebastian Dalleiger

This fully funded doctoral position at KTH Royal Institute of Technology offers an exciting opportunity to pursue a joint PhD degree in models, algorithms, and optimization for machine learning, in collaboration with Nanyang Technological University (NTU), Singapore. The successful candidate will be formally enrolled at KTH and will spend at least 12 months at NTU as part of the joint program, benefiting from supervision by Professor Aristides Gionis (KTH), Associate Professor Kelly Ke Yiping (NTU), and Assistant Professor Sebastian Dalleiger (KTH). The research project is broadly situated within computer science, focusing on advanced machine learning topics such as algorithmic knowledge discovery, graph mining, social network analysis, optimization for machine learning, representation learning, and fair, accountable, and transparent machine learning. The position is ideal for candidates with a strong interest in computational sciences, applied mathematics, and statistics, and who are eager to contribute to cutting-edge research in these areas. Applicants must hold or be about to receive a Master of Science degree in computer science, machine learning, AI, data science, or a related field. Alternatively, candidates may qualify with at least 240 higher education credits, including 60 at the second-cycle level, or equivalent knowledge. English proficiency equivalent to English B/6 is mandatory. The selection process emphasizes academic excellence, strong background in algorithms, machine learning, optimization, programming skills, and personal attributes such as goal orientation, perseverance, independence, collaboration, and professional approach. The position is fully funded, offering a monthly salary according to KTH’s doctoral student salary agreement, along with employee benefits and a supportive workplace environment. The employment is temporary, full-time, and may be renewed according to KTH regulations. The doctoral student will have access to a creative and dynamic research environment, with opportunities for professional growth and development. To apply, candidates must submit their application through KTH's recruitment system, ensuring all required documents are included: CV, application letter, diplomas, grades, certificates of language requirements, and representative publications or technical reports. Applications must be received by the deadline of March 5, 2026. For further information, contact Professor Aristides Gionis ([email protected]). KTH Royal Institute of Technology is a leading international technical university, committed to advancing education, research, and innovation for a sustainable society. The university values equality, diversity, and equal opportunities, providing an inclusive environment for all students and staff.

A fully funded PhD position is available in models, algorithms, and optimization for machine learning, leading to a joint doctoral degree from KTH Royal Institute of Technology (Sweden) and Nanyang Technological University (Singapore). The research project is situated in the field of machine learning, with potential topics including algorithmic knowledge discovery, graph mining, social network analysis, optimization for machine learning, representation learning, and fair, accountable, and transparent machine learning. The successful candidate will be supervised by Professor Aristides Gionis and Assistant Professor Sebastian Dalleiger at KTH, and Associate Professor Kelly Ke Yiping at NTU. The doctoral student will be recruited and formally enrolled at KTH, with a requirement to spend at least 12 months at NTU as part of the joint program. Applicants must hold or be about to receive a Master of Science degree in computer science, machine learning, AI, data science, or a related area. A strong background in algorithm design, machine learning, and optimization, as well as strong programming and implementation skills, is essential. English proficiency equivalent to English B/6 is mandatory. Candidates should be highly self-motivated, able to work independently and collaboratively, and committed to publishing and presenting high-quality research. The position is fully funded, offering a monthly salary according to KTH's doctoral student salary agreement, along with employee benefits. The total length of employment corresponds to full-time doctoral education for four years. The application deadline is March 5, 2026. To apply, candidates must submit a complete application through KTH's recruitment system, including a CV, application letter, diplomas, grades, language certificates, and relevant publications. All documents must be certified and translated if necessary. For more information, visit the official KTH job posting or contact Professor Aristides Gionis at [email protected]. Keywords: machine learning, algorithms, optimization, graph mining, social network analysis, representation learning, computer science, KTH, NTU, PhD, Sweden, Singapore.

This fully funded doctoral position at KTH Royal Institute of Technology offers an exciting opportunity to pursue a joint PhD degree in models, algorithms, and optimization for machine learning, in collaboration with Nanyang Technological University (NTU), Singapore. The successful candidate will be formally enrolled at KTH and spend at least 12 months at NTU as part of the joint program, benefiting from supervision by Professor Aristides Gionis (KTH), Associate Professor Kelly Ke Yiping (NTU), and Assistant Professor Sebastian Dalleiger (KTH). The research project is situated within the broad field of machine learning, with potential topics including algorithmic knowledge discovery, graph mining, social network analysis, optimization for machine learning, representation learning, and fair, accountable, and transparent machine learning. The position is ideal for candidates with a strong interest in advancing the theoretical and practical aspects of machine learning and computational sciences. Applicants must hold or be about to receive a Master of Science degree in computer science, machine learning, AI, data science, or a related area. Essential qualifications include a solid background in algorithm design, machine learning, and optimization, as well as strong programming and implementation skills. English proficiency equivalent to English B/6 is mandatory. Candidates should demonstrate strong academic credentials, excellence in coursework or relevant projects, and personal skills such as goal orientation, perseverance, independence, collaboration, professionalism, and the ability to analyze complex issues. The position is fully funded, with a monthly salary according to KTH’s Doctoral student salary agreement, and includes employee benefits and a workplace at KTH. The doctoral student will be employed for a maximum of one year initially, with possible renewals up to four years in total. The role may include up to 20% training and administrative tasks. The research environment at KTH is creative and dynamic, offering opportunities for growth and development, and is committed to equality, diversity, and sustainability. To apply, candidates must submit their application through KTH's recruitment system, including diplomas, grades, certificates of language requirements, CV, application letter, and representative publications or technical reports. Certified translations are required if documents are not in English or Swedish. Applications must be received by the closing date of June 25, 2026. For further information, contact Professor Aristides Gionis ([email protected]). This position provides a unique opportunity to work at the intersection of machine learning, algorithms, and optimization, and to earn a joint PhD degree from two leading international technical universities. Join KTH and NTU to shape the future of machine learning research!

KTH Royal Institute of Technology is advertising a fully funded PhD position in models, algorithms, and optimization for machine learning , leading to a joint doctoral degree from KTH (Sweden) and Nanyang Technological University (Singapore) . The project is broadly situated in computer science and machine learning, with possible research directions including algorithmic knowledge discovery , graph mining , social network analysis , optimization for machine learning , representation learning , and fair, accountable, and transparent machine learning . The doctoral student will be recruited and formally enrolled at KTH and will spend at least 12 months at NTU in Singapore as part of the joint program. Supervision is proposed by Professor Aristides Gionis (KTH), Assistant Professor Sebastian Dalleiger (KTH), and Associate Professor Kelly Ke Yiping (NTU). Eligibility highlights include a Master of Science degree by enrollment time, or equivalent qualifying credits/knowledge, plus English B/6 or equivalent. The call also emphasizes strong academic credentials, a solid background in algorithms design , machine learning , and optimization , along with strong programming and implementation skills. Funding is described as a monthly salary according to KTH’s doctoral student salary agreement , with standard employee benefits. This is a doctoral employment rather than a scholarship. Deadline: 25 June 2026. Applications must be submitted through KTH’s recruitment system and include diplomas, transcripts, proof of language requirements, CV, a motivation/application letter, and representative publications or technical reports.