【简介】博士后招聘网整理分享“美国阿尔贡国家实验室招聘计算机方向博士后”，浏览查询更多博士后招聘计划请访问博士后招聘网。

Interested in working on some of the largest supercomputers in the world? Would you like to help model, analyze, and design the next generation of extreme scale systems and their workloads? Then come join the Performance Engineering team at the Argonne Leadership Computing Facility to design and development new approaches to performance evaluation and performance analysis of scientific and data-centric workloads. The successful candidate will draw on knowledge of high performance computing, computational science, compilers and runtime systems, and optimization techniques, to introduce new methods and tools that substantially improve the performance of a range of computational science and data-centric workloads that can be addressed by exascale computing. You will also partner with computational science teams, other high-performance computing centers or tool development efforts to address common challenges of the pre-exascale era. 

Position Requirements:
• A Doctorate and 0 years of work experience in high performance computational science, particularly in one or more of these fields: computational biology, chemistry, earth science, engineering, materials science, nuclear energy, physics, renewable energy, energy science, mathematics, computer science.
• Considerable knowledge of performance evaluation, tuning, projections, and simulation techniques.
• Considerable knowledge of parallel programming models and algorithms, compiler frameworks, performance tools.
• Considerable software development skills, expertise to create high-quality software and ability to program with various languages, such as Fortran, C/C++, Python, Perl, shell.
• Performance modeling and projections with goal of understanding and projecting an application`s performance on existing and future high-performance platforms.
• Target hardware features include but are not limited to accelerators, multi-core architectures, processor-in-memory, memory and interconnect subsystems, and energy efficiency.
• Research and develop new modeling techniques and apply them to computational science applications.

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