1. Rutgers Discovery Informatics Institute (RDI2) (http://rdi2.rutgers.edu/) 直属Rutgers University The office of Research and Economic Development. Director Manish Parashar为CS department的distinguish professor, IEEE fellow, AAAS fellow.|
2. 欢迎有计算机背景的、符合下述招生要求的Computer Science或是ECE的学生。
3. 申请流程请继续遵循正常Computer Science和Electrical and Computer Engineering的研究生院申请途径。
5. 有兴趣的请将简历发到: firstname.lastname@example.org
The Rutgers Discovery Informatics Institute (RDI2) have several open positions for qualified Ph.D. students and PostDocs in the broad area programming and runtime systems for large scale parallel and distributed computing.
Extreme Scale Computing and Data Management:
Working as part of the DataSpaces Project ([url=http://www.www.dataspaces.orgdataspaces.org/]www.www.dataspaces.org[/url]) successful candidates will join an ambitious, multidisciplinary teams focused on designing conceptual and software solutions for managing computation and data on the largest computing system in the world, and enabling cutting-edge extreme-scale science.
This project provides the opportunity to interact across several STEM fields, as well as work with national and international collaborators from academia, national laboratories and industry. Successful candidates will also have the opportunity to work with real-world simulations and experiments running on world leading edge computing systems. Specific research topics include programming system for in-situ scientific workflows, scalable multi-tiered object stores for extreme scale systems, and application-level resilience.
- Candidates should have a strong background in operating systems and systems programming in C and/or C++, and must be familiar with parallel programming principles.
- Previous experience in shared-memory parallelization (OpenMP), distributed-memory parallelization (MPI), and/or GPU-based methods (e.g. CUDA, OpenCL) is a plus.
- Application areas include, but are not limited to exa-scale machine learning/data mining methods on big medical data(e.g. image, gene sequence data), high energy physics and computational chemistry.
- Applicants with any experience in machine learning algorithms, neural networks and computer vision are also welcomed.