Research Fellow in Computer Science with Focus on AI and Machine Learning Optimization
Nanyang Technological University Singapore
Singapore · Full Time
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- Experience
- Any
- Salary
- —
- Openings
- 1
- Posted
- 1 week ago
- Work mode
- In office
- Education
- PhD
- Eligibility
- Open to candidates holding a PhD in Computer Science, Artificial Intelligence, Machine Learning, or closely related areas with a strong research background as per listed requirements.
- Resume
- Required to apply
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Job description
About the Institution
Nanyang Technological University (NTU) Singapore is a young, research-intensive university recognized globally among the top institutions. The College of Computing and Data Science (CCDS) at NTU stands out for its cutting-edge curriculum, high-impact research initiatives, and renowned faculty members. Positioned at the heart of Asia, CCDS offers a dynamic environment for learning and innovation, nurturing future leaders, thinkers, and pioneers in AI, Data Science, and Computing.
Key Responsibilities
- Execute independent research focused on deep learning techniques, large-scale model optimization, and understanding generalization aspects.
- Investigate scalable optimization strategies for training models across large-scale, distributed, and multi-node collaborative computing environments.
- Conduct theoretical work analyzing optimization, convergence behavior, stability, and generalization to guide practical training algorithm designs.
- Publish rigorous research findings in leading machine learning conferences and journals.
- Mentor and support students participating in related research projects.
- Collaborate with both academic peers and industry partners, while fulfilling other duties linked to the research program.
Candidate Requirements
- Possession of a PhD in Computer Science, Artificial Intelligence, Machine Learning, or closely related areas.
- Established theoretical research expertise especially in analyzing optimization, convergence, stability, or generalization of deep learning models.
- Strong practical knowledge of machine learning optimization methods, including distributed or collaborative multi-node training; experience with large-scale training environments is greatly advantageous.
- A robust portfolio of publications in premier venues such as ICML, ICLR, NeurIPS, and IEEE TPAMI.
- Proven ability to identify fundamental research problems, invent innovative optimization methods, and lead comprehensive theoretical and empirical investigations.
- Highly analytical, self-motivated, and capable of collaborating effectively within a research team.
Additional Information
Only shortlisted candidates will be contacted for the next steps.
Minimum education
Doctorate