The Institute of Data Science at National University of Singapore (NUS) is looking for multiple postdoctoral Research Fellows to work on machine learning (ML) and natural language processing (NLP) research for indigenous/vernacular languages. The project is a newly-funded international collaboration between Singapore and New Zealand on data science, focusing on multi-lingual Q&A for revitalising and facilitatng indigenous/vernacular languages in Southeast Asia and New Zealand through AI and data science. The NUS team, led by Professors See-Kiong Ng and Stephane Bressan, will be working closely with the NZ team led by Professors Ruili Wang (Massey University), Michael Witbrock (Auckland University) and Te Taka Keegan (University of Waikato) in this 3-year project.
The Research Fellows will be responsible for undertaking novel research in ML and NLP, focusing on multi-lingual Q&A for low-resource languages. The research should lead to publications in top-tier international conferences and journals, new research resources such as well-annotated NLP datasets, and implementations in real-world applications.
The initial appointment will be for one year, with the possibility to extend based on performance. Selected candidates will be offered with attractive/competitive salaries and benefits. If interested, please send your resume and a cover letter to Professor See-Kiong Ng at .
– Research and develop new ML-based methodologies and algorithms in NLP for multi-lingual Q&A in low-resourced languages
– Design and implement data collection for NLP research for regional indigenous/vernacular languages
– Participate in knowledge exchange and knowledge transfer activities with external partners and collaborators
– Mentor research engineers and postgraduate students in the team
– A PhD in Computer Science or equivalent, with specialization in machine learning and natural language processing;
– Proven ability to conduct independent research with a strong and relevant publication record;
– Prior experience in computational linguistics or NLP, and state-of-the-art ML techniques such as few-shot learning and transfer learning would be a plus;
– Experienced in working in a team, with people of diverse skillsets and cultural background.
– Excellent interpersonal communication and oral presentation skills in English;