PhD Position in Machine Learning - Joint Degree from KTH (Sweden) and NTU (Singapore)
Join a fully-funded PhD program in machine learning with dual enrollment at KTH and NTU.
Best for:Ideal for candidates with a strong background in algorithms and machine learning seeking international research experience.
Overview
This position offers a motivated candidate the opportunity to pursue a fully-funded PhD in machine learning, resulting in a joint degree from KTH in Sweden and NTU in Singapore. The selected student will be enrolled at KTH and will spend a minimum of 12 months at NTU, under the guidance of co-supervisors from both institutions. The research will focus on developing new algorithms, designing methods, and analyzing experimental results in various areas of machine learning. Candidates should be passionate about advancing the field and committed to publishing in top-tier conferences.
What you'll do
- Develop new algorithms and design robust methods in machine learning.
- Formalize theoretical foundations of machine learning techniques.
- Conduct experimental analyses to evaluate the effectiveness of proposed methods.
- Collaborate with supervisors and peers across both KTH and NTU.
Requirements
Degree required: Master's degree in Computer Science, Mathematics, Physics, or a related field (completed or expected before start date)
Degree offered: PhD
Language: English; proficiency in academic writing and communication
Instruction: English
Publications: Commitment to publishing in top-tier venues is expected
- Strong background in algorithm design and machine learning
- Experience in mathematical optimization and graph algorithms
- Proficient programming skills for implementing and benchmarking algorithms
- High motivation to publish in leading conferences such as NeurIPS, ICML, ICLR, KDD
- Open to all nationalities
- Must hold a relevant Master's degree or be close to completion
Funding & terms
How to apply
Deadline: 2026-10-22
Interested candidates should apply through the provided link. Ensure all required documents are submitted by the deadline.
- CV
- Cover letter
- Master's degree transcripts
- 2 reference letters
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