Doctoral student in machine learning and optimization Full-time NEW

Doctoral Student In Machine Learning And Optimization

KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science invites talented and highly motivated candidates to apply for this doctoral position. The doctoral student will develop new algorithms and well-founded methods, formalize their theoretical foundations, and evaluate them experimentally. The research will be broadly situated in machine learning, including (but not limited to) algorithmic knowledge discovery, graph mining and social network analysis, optimization for machine learning, representation learning, and fair, accountable, and transparent machine learning.

This PhD position in machine learning, will lead to a joint doctoral degree from KTH Royal Institute of Technology (Sweden) and Nanyang Technological University (NTU, Singapore). The successful candidate will be enrolled as a doctoral student at KTH and will spend at least 12 months at NTU as part of the program.

Supervision: Aristides Gionis (KTH), Sebastian Dalleiger (KTH) and Kelly Ke Yiping (NTU)

To be admitted to postgraduate education, the applicant must have basic eligibility in accordance with either of the following: passed a second cycle degree (for example a master's degree), or completed course requirements of at least 240 higher education credits, of which at least 60 second-cycle higher education credits, or acquired, in some other way within or outside the country, substantially equivalent knowledge. The candidate must be able to commit to a minimum 12-month research stay at NTU in Singapore. This is a requirement for this specific position. In addition to the above, there is also a mandatory requirement for English equivalent to English B/6.

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 independently pursue his or her work, collaborate with others, have a professional approach and analyze and work with complex issues. The Applicant must hold or be about to receive a Master of Science degree in computer science, machine learning, AI, data science, or a related area. Applicants must have strong academic credentials, demonstrated by excellence in course work or relevant projects. The ideal candidate is highly motivated with a solid background in one or more of the following areas: algorithm design, machine learning, mathematical optimization, and learning theory. The candidate should also have strong programming skills for implementing and benchmarking algorithms, and be self-driven and committed to producing and presenting high-quality research at top-tier venues such as NeurIPS, ICML, ICLR, and KDD. After the qualification requirements, great emphasis will be placed on personal skills.