Research Fellow, AI (School of Computing)

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Fellow%2C-AI-%28School-of-Computing%29/33616-en_GB/?st=E1005F5827666D480FD26C67D7E4512EE09CBF76

We regret that only shortlisted candidates will be notified.

Job Description

The National University of Singapore invites applications for the position of Research Fellow in the Department of Computer Science, School of Computing (SoC). SoC is strongly committed to research excellence in all its dimensions: Searching for fundamental results and insights in developing novel computational solutions to a wide range of applications, building largescale experimental systems, developing theories and policies for effective management of information systems in organizations, and improving the well-being of society. We seek to play an active role both internationally and locally in the core and emerging areas of Computer Science and Information Systems.

The candidate will conduct independent and collaborative research in AI-driven computational biology, with a focus on protein modeling, drug discovery, molecular representation learning, large-scale biomedical data integration, and foundation models for biological systems. The appointee will develop novel machine learning methods, contribute to software and database development, publish high-quality research papers, and assist in supervising junior researchers and students.

Qualifications

PhD in Computational Biology, Computer Science, Bioinformatics, Biomedical Engineering, or a related discipline. Strong background in machine learning, deep learning, graph neural networks, and computational biology. Experience in protein modeling, molecular representation learning, biomedical knowledge graphs, and large-scale biological data analysis. Excellent programming skills and demonstrated research productivity through peer-reviewed publications.