Research Fellow – Probabilistic Machine Learning & GenAI

About the role

Dr Yingzhen Li’s research group at NTU is seeking a Research Fellow to conduct research in probabilistic machine learning, causal discovery and Generative AI. The work will focus on developing new architectures for latent dynamic modelling and exploring emerging directions in AI.

Key responsibilities

• Conduct research in AI/ML domains, especially probabilistic ML and scalable sequence network architectures.
• Produce academic publications such as research papers and patent applications.
• Contribute to annual report writing and present findings.
• Collaborate with faculty, research staff, and students on joint robotics and AI projects and support supervision of graduate students.
• Engage with industry or academic partners to explore collaborative opportunities and contribute to project deliverables.

Required profile

• PhD degree in Computer Science, Statistics, Electronics Engineering or equivalent.
• Independent, highly analytical, proactive and a team player.
• Excellent teamwork and verbal, written communication skills.
• In‑depth knowledge relevant to the project domain.
• Demonstrated capability to conduct innovative research.
• Ability and willingness to adapt and incorporate novel ideas and approaches.
• Demonstrated ability to formulate hypotheses and design effective experimental plans.
• Ability to design and implement new experimental methods.
• High‑level expertise in required experiment or modelling methods.
• Ability to initiate collaborative research in a multidisciplinary team.
• Oversee and report project progress.
• Willingness to work flexible hours.

Required skills

• Probabilistic Machine Learning
• Causal Discovery
• Generative AI
• Causal Representation Learning
• Dynamical Systems
• Sequence Generative Models
• World Modelling
• Latent Dynamic Modelling
• Scalable Sequence Network Architectures