The University of California, Merced, is the newest of the University of California system’s 10 campuses. UC Merced offers an environment that combines a commitment to diversity, inclusion, collaboration and professional development. Ranked among the best public universities in the nation by U.S. News and World Report, UC Merced is uniquely equipped to provide educational opportunities to highly qualified students. UC Merced is approximately two-hour drive from the Silicon Valley and San Francisco Bay Area, one hour from the Yosemite National Park, and 1-2 hour drive from the Lawrence Berkeley National Lab and Lawrence Livermore National Lab.
In Fall 2016, UC Merced broke ground on a $1.3 billion public-private partnership that is unprecedented in higher education. The Merced 2020 Project will nearly double the physical capacity of the campus by 2020, enhancing academic distinction, and student success and research excellence. UC Merced is also building the Downtown Campus Center, a $33 million, three-story administrative building in the heart of Merced.
Parallel Architecture, System, and Algorithm lab (PASA) directed by Dr. Dong Li, is looking for a self-motivated, creative, enthusiastic and highly qualified postdoctoral scholar expert in high performance computing to work on a project on improving performance and energy efficiency of machine learning workloads on heterogeneous parallel systems (GPU and FPGA), and applying machine learning to improve performance of HPC applications. The PASA lab is currently investigating a variety of problems related to runtime, architecture, performance modeling, and programming models for parallel systems. More information can be found at (http://pasa.ucmerced.edu/).
The successful candidate will have previous training and established track record in accelerator programming (e.g., GPU and FPGA), performance optimization, and runtime design. The postdoctoral researcher is expected to stay abreast of recent publications in the field, carry out research independently, implement and develop research ideas and publish research results in leading conferences and journals. The postdoctoral scholar is also expected to assist with managing the project, directing the research of undergraduate and graduate students, coordinating research across the team, and preparing project deliverables for the sponsor. Research funding for the post-doc position is available for 24 months, with a likely extension based on continuation of funding and satisfactory performance.
Qualifications: Applicants should have a PhD degree in computer science, computer engineering, or a related field, conferred by the start date of the position. Strong written and verbal communication skills are required. Highly self-motivated individuals who have demonstrated the ability to conduct scientific research successfully and work independently are desirable.
The candidate should have demonstrable experience in at least two, preferably more, of the following areas: 1) machine learning theory (e.g., neural network, regression, and SVM); 2) CUDA and OpenCL; 3) compiler-based static analysis (e.g., LLVM and Rose); 4) Verilog or VHDL for programming FPGA; 5) machine learning framework (e.g., tensorflow and Caffe2).
Applicants should send a one-page cover letter summarizing their qualifications and research accomplishments, a CV, and contact information for three references as a single pdf document electronically with the subject line "Postdoctoral application" to Dr. Dong Li at email@example.com.
Salary is based on the University of California Academic Salary Scales. Review of applications will begin immediately. The deadline for applications is Jan 30, 2019, with applications continuing to be considered until the position is filled. Starting dates are negotiable.
Please contact Dong Li at firstname.lastname@example.org for additional information.