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

The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.