Research Assistant - AGI & Embodied Intelligence

NATIONAL UNIVERSITY OF SINGAPORE; Singapore; On-site; SGD 45,000 - 67,000; Full time

Job summary

National University of Singapore invites applications for Research Assistant positions in the Learning and Vision Lab within the School of Computing. The role focuses on developing novel computational architectures for foundation models, exploring embodied AI, and pursuing self-improving methodologies that push the boundaries of intelligent systems.

The successful candidate will contribute to literature review, algorithm development, and prototype creation, working under experienced mentors to

Qualifications

• Strong commitment to academic and research excellence.
• Solid understanding of large language models, with practical experience using deep learning libraries such as PyTorch, TensorFlow, JAX, or equivalents.
• Demonstrated ability to collaborate effectively with fellow researchers.
• A penchant for unconventional thinking as a catalyst for creative and innovative solutions.

Responsibilities

• Develop a comprehensive understanding of diverse research domains and relevant literature, mastering a wide range of research techniques.
• Identify tractable components within complex research problems, showcasing exceptional problem-solving skills.
• Investigate emerging tools, technologies, and methodologies under the guidance of experienced mentors, contributing specialized expertise to effective project planning.
• Successfully complete research tasks that yield innovative outcomes.
• Develop algorithms, prototypes, theoretical frameworks, tools, analyses, insights, or datasets that address critical aspects of our overarching research objectives.

Skills

academic excellence

collaborative skills

unconventional thinking

Tools

PyTorch

TensorFlow

JAX

Job description

National University of Singapore invites applications for Research Assistant positions in the Learning and Vision Lab within the School of Computing. The role focuses on developing novel computational architectures for foundation models, exploring embodied AI, and pursuing self-improving methodologies that push the boundaries of intelligent systems.

The successful candidate will contribute to literature review, algorithm development, and prototype creation, working under experienced mentors to