Scientist, CFAR, IHPC

We are seeking highly motivated scientists with a strong research inclination to conduct fundamental studies on foundation models, including diffusion models and large language models.

Job Responsibilities:

• Conduct fundamental research on foundation models to understand their capabilities, such as optimization, approximation, and generalization.
• Develop theoretically grounded learning methodologies and applications aimed at improving the reliability of foundation models.
• Publish impactful scientific results in top-tier machine learning and AI conferences or journals, such as NeurIPS, ICML, ICLR, AISTATS, AAAI, IJCAI, JMLR, and AoS, in collaboration with scientists at A*STAR IHPC.

Requirements:

• PhD degree in Computer Science, Mathematics, Statistics, or Computer Engineering.
• Research experience in machine learning or related areas.
• Strong mathematical background relevant to machine learning and deep learning, including areas such as optimization theory, statistical learning theory, stochastic differential equations, optimal transport, control theory, approximation theory, and geometry.
• Knowledge in at least one deep learning framework, such as PyTorch, TensorFlow, or JAX.
• Strong problem-solving and analytical skills.
• Excellent communication and presentation skills.