Scientist, CFAR, IHPC

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.
Type of Employment : Full-Time
Work Location : Fusionopolis