• Engaging in scientific AI research including reading recent scientific papers in related fields, conducting experiments on GPU clusters and writing academic papers. • Combining AI technology with industrial scenario and developing AI applications. This internship will mainly focus on neural operators for modeling solutions to differential equations, including those trained with physics-informed loss or data based loss. Other AI research topics can also be considered based on mutual interest. Qualification: 1. PhD candidates in computer science, mathematics, physics, or related engineering fields are preferred. Candidates in master programs with excellent mathematical and programming skills are also welcomed. 2. Proficiency in Python and at least one deep learning library including Pytorch, Tensorflow, Jax. Experience in GPU computing is preferred. 3. Strong background in theory of statistical machine learning and familiarity with various architectures in deep learning including neural operators, VAE, diffusion, transformers, etc. 4. Capability of independent work, passion in AI research and good communication skills.