Parallel Algorithm Scientist

- Parallel computing: Familiar with main techniques developing status, hotspots and trends about parallel computing in industry and academic, and have outstanding experience;
- Research and project experiences are expected to include several of the following topics:

The design, analysis and implementation of efficient (including general purpose) parallelalgorithms.

The design and optimization of effective programming languages, models and methods for parallel programs.

The design and analysis of parallel and high performance computing systems, such as SMP,cluster, and multi-core systems.

The development of algorithms and environments for parallel-, cluster-, heterogeneous and multi-core computing. Integration of parallel computing systems to modern infrastructures such as grids and

Clouds.

Programming environments (including tools for semi- or automatic parallelization) for efficient development of parallel programs.

- Concurrent and parallel programming languages: familiar with popullar concurrent and parallel programming languages, such as MPI, OpenMP, OpenCL, erlang, go, and experience with related projects
- Parallel algorithmic methods: Strong knowledge of The design, analysis and implementation of efficient (including general purpose) parallel algorithms, especially telecom/network domain related algorithms,and bigdata related is also OK.