TeCSAR is committed to open-source code development and sharing with the scientific community. We release most of hardware and software codes and simulation tools developed at TeCSAR lab with respect to intellectual property conditions and restrictions of funding agencies. This is an important step to enable the broader research community to reproduce results and knowledge developed at TeCSAR lab.
\nLink to TeCSAR repository:<\/p>\n\n\n\n
GitHub for TeCSAR-UNCC<\/a><\/p>\n\n\n\n I am continuously searching for talented, self-motivated Ph.D. and M.Sc. students. If you are interested in joining TeCSAR and working with me, please read the following:<\/p>\n\n\n\n As a Ph.D. student, you will be involved in serious research tasks including critical thinking, rigorous analysis, and scientific writing. If you are interested, do not hesitate to write an email to htabkhiv@charlotte.edu<\/strong><\/span><\/span>. Please briefly express your reasoning on why you are interested in joining TeCSAR. Make sure to attach your academic CV and one of you recent publications to your email!<\/p>\n\n\n\n At TeCSAR, we have plenty of opportunities for Master Students. Overall, master students will directly work with state-of-the-art boards including Xilinx UltraScale and NVIDIA Jetson platforms. This includes learning DMAs, AXI buses, real-time OS and embedded software development, CUDA and OpenCL programming. <\/p>\n","protected":false},"excerpt":{"rendered":" TeCSAR Repository TeCSAR is committed to open-source code development and sharing with the scientific community. We release most of hardware and software codes and simulation tools developed at TeCSAR lab with respect to intellectual property conditions and restrictions of funding … Continue reading Interested Students<\/h3>\n\n\n\n
For PhD. Applicants<\/h3>\n\n\n\n
\nThe primary research focus will be around architecture innovation for emerging applications (machine learning, deep learning, and machine vision), Internet of Things (IoT) and Cyber-Physical Systems (CPS). At TeCSAR, you will face plenty of learning opportunities from algorithm design and system-level modeling down to hardware\/software co-design and architecture implementation.
\nDepending on the research project, background in Hardware Description Languages (preferably Verilog-HDL), C\/C++ programming, Python and scripting languages are required. CUDA and OpenCL programming skills or background in computer vision, and machine learning and deep learning is a HUGE advantage. In addition, the core knowledge of computer architecture and algorithm design is a MUST!<\/p>\n\n\n\nFor Interested M.Sc. Students<\/h3>\n\n\n\n
If you are interested in working with me, send an email to htabkhiv@charlotte.edu<\/span> with your resume attached. I only advise master students who are interested in pursuing Thesis. In your email, please explicitly mention the projects you are interested in. You can find my active projects<\/a>.<\/p>\n\n\n\n