微软亚洲研究院系统组研究型实习生招聘，欢迎有意向的同学发送简历至kunli@microsoft.com
Position ID: FY23Q2J3
Location: Building 2, No. 5 Dan Ling Street, Haidian District, Beijing
Research Direction: Next generation scientific computing system
Duration: At least 3 month
Deadline: Long-term effective
What You Will Do:
- Develop next generation scientific computing system
- Build modern tools for scientists to easily conduct large-scale scientific computing
- Implement widely used scientific computing algorithms and numerical solvers
- Document work and results in the form of journal papers and conference proceedings
- Present work and results at scientific meetings
What is Required:
- Familiar with Python and C/C++
- Familiar with common productive tools, including Linux, Git, SSH, Docker, etc.
- Experience of developing popular numerical methods, such as FDM, FEM, FVM, etc.
- Experience of developing distributed code with MPI
- Excellent written and oral communication skills
Desired Qualifications:
- Experience with PyTorch or other deep learning framework is a plus
- Experience with popular neural network models, like CNN or GNN, is a plus
Position ID: FY23Q2J4
Location: Building 2, No. 5 Dan Ling Street, Haidian District, Beijing
Research Direction: AI for science and scientific computing
Duration: At least 3 month
Deadline: Long-term effective
What You Will Do:
- Solve complex ordinary/partial differential equations with deep learning techniques
- Solve the most challenging scientific problems with deep learning techniques
- Document work and results in the form of journal papers and conference proceedings
- Present work and results at scientific meetings
What is Required:
- Familiar with one of machine learning applications, such as CV, NLP, Graph, etc.
- Familiar with popular deep learning frameworks, such as PyTorch and Tensorflow.
- Familiar with commonly used productive tools, including Linux, Git, SSH, Docker, etc.
- Excellent written and oral communication skills
Desired Qualifications:
- Science-related background, including but not restrict to math, physics, materials, chemistry, meteorology, etc., is a plus
- Experience with Navier-Stokes equation, Schrodinger equation or density functional theory is a plus
- Experience with recent deep learning methods to solve PDEs, like PINN, FNO or DeepONet, is a plus
- Publication on top machine learning conferences or journals is a plus.
Desired Qualifications:
- Science-related background, including but not restrict to math, physics, materials, chemistry, meteorology, etc., is a plus
- Experience with Navier-Stokes equation, Schrodinger equation or density functional theory is a plus
- Experience with recent deep learning methods to solve PDEs, like PINN, FNO or DeepONet, is a plus
- Publication on top machine learning conferences or journals is a plus