Computer Engineering Researcher, ASIC and FPGA Job Order 3418
Seven Corners, VA 22041
Hybrid, Tuesday Thursday on-site
US citizenship
$155K to $196K
Ph.D. in Electrical Engineering or Computer Engineering
This organization has an opening for a Computer Engineering Researcher to research and develop new hardware solutions for problems of national importance.
The Researcher will collaborate with peers within the group and across the organization, propose major innovations, develop prototypes, oversee laboratory demonstrations, publish results in top-tier conferences, and learn to craft their ideas into winning proposals.
The Researcher will develop advanced computer architectures and implement designs on applied research programs from top research sponsors such as DARPA, IARPA, and the CHIPS Act.
The Researcher will additionally have opportunities to use resurgent MOSIS 2.0 service, interact with the California DREAMS superhub, and collaborate with the Application-Specific Intelligent Computing Lab to architect systems building upon CMOS and Beyond CMOS devices.
They provides a unique environment for leading-edge government-funded applied research with strong ties to academia that include the pioneering Frontiers in Computing and School of Advanced Computing, and potential paths to becoming Research Faculty.
Qualifications
High-quality publications related to computer architecture, chip design, or prototyping
Strong computer hardware design experience (Verilog / System Verilog / VHDL)
Experience with system modeling using python, C++ or other
Ability to work in our Seven Corners, VA office on a hybrid basis
Qualified candidates for this position must be willing and eligible to apply for a Top Secret clearance. Eligibility for this clearance requires U.
S. citizenship. Current SECRET clearance or higher is a plus.
Preferred Job Qualifications :
Experience contributing to proposals
Experience with FPGA-based development or application mapping
Adeptness with EDA tools and scripting
Understanding of graph-based algorithms or other EDA algorithms
Experience with neuromorphic or other non-von Neumann approaches
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