Microsoft Research Asia Full-time Internship Positions

Position 1

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 2

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

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