Posted:
12/25/2024, 12:07:11 PM
Location(s):
Singapore, Singapore
Experience Level(s):
Mid Level ⋅ Senior
Field(s):
AI & Machine Learning ⋅ Data & Analytics
Workplace Type:
On-site
Young and research-intensive, Nanyang Technological University, Singapore (NTU Singapore) is ranked among the world’s top universities. NTU’s College of Computing and Data Science (CCDS) is a leading college that is known for its excellent curriculum, outstanding and impactful research, and world-renowned faculty. Today, we are ranked #2 for AI and Computer Science by US News Best Global Universities; and #8 for Data Science and AI by QS World University Ranking.
A hot bed of cutting-edge technology and groundbreaking research, the College aims to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and grow'. We welcome you to join our community of faculty, students and alumni who are shaping the future of AI, Data Science and Computing.
We seek to appoint a postdoctoral Research Fellow (fixed term, 30 months) to work on uncertainty quantification in LLMs.
Key Responsibilities:
Manage own academic research and administrative activities. This involves small scale project management, to co-ordinate multiple aspects of the work to meet deadlines.
Develop independent research questions, propose new methodologies to improve on existing methods and contribute ideas for collaborative research projects.
Assist the PI in mentoring, supervising and coordinating the activities of junior researchers and students of the research project.
Lead small teams of students, software engineers, research assistants and other junior researchers to formulate and address research questions within the purview of the project; and assist the PI in mentoring and supervising them.
Act as a source of information and advice to other members of the research team.
Publish and present papers at conferences or public meetings.
Work with industry and government partners to understand their research needs and formulate solutions.
Contribute to outreach, communications and reporting as required to funders and stakeholders. Represent the research group at external meetings/seminars, either with other members of the group or alone.
Help organise and actively participate in regular meetings and seminars within the group.
Assist in raising research funds through grant applications.
Carry out some related teaching and other works for the College that takes into account their career aspirations and development goals.
Job Requirements:
Essential Criteria
Close to completion or hold a relevant Ph.D. with post-qualification research experience in statistical machine learning, and deep learning or large language models.
Strong publication record and familiarity with the existing literature and research in the field of statistical machine learning, and deep learning or large language models.
Possess sufficient specialist knowledge, research skills and interests in statistical machine learning, and deep learning or large language models to work within established research programmes and contribute ideas and solutions.
Ability to independently plan and manage a research project, including a research budget.
Possess strong collaboration abilities in person, on paper and over electronic channels.
Excellent communication skills, including the ability to write for publication, present research proposals and results, and represent the research group at meetings.
Desirable Criteria
Possess strong technical knowledge and abilities of deep learning and large language modelling frameworks (pytorch, jax, huggingface) as well as popular open-source codebases and LLM APIs.
Experience of supervising staff.
Experience of managing a research budget.
Ability to raise research funds through making grant applications.
We regret that only shortlisted candidates will be notified.
Hiring Institution: NTUWebsite: https://ntu.edu.sg/
Headquarter Location: Singapore, Central Region, Singapore
Year Founded: 1991
Last Funding Type: Grant
Industries: Education ⋅ Information Technology ⋅ Universities