Hardware Based Machine Learning for Neural Signal Processing
Biomedical Engineering/Electronics
Machine learning allows us to classify data to find patterns of interest. By analyzing neural signals recorded from the brain, it is possible to recognize abnormal activity such as a seizure in the case of epilepsy patients. At the Intelligent Sensory Microsystems Lab, we have been researching the implementation of efficient machine learning processors in hardware and integrating this technology with neural monitoring and stimulation ASICs. This allows for a closed loop system which can provide effective and adaptable treatment for patients.
Expectations
Depending on your experience and interests, you may be expected to implement models of signal processing algorithms, implement new designs on an FPGA, perform literature reviews or explore a related area of your own choosing. It is expected that you will be self motivated with guidance from an experienced graduate student at the lab. If you are considering a future in research, we will help you to discover what area resonates with you the most.
Time commitment
3-10 hours per week
Credentials
FPGA/Verilog, MATLAB and Machine learning experience, and an interest in neuroscience is desirable.
Positions available
1-3 positions available
Applications for 2018-2019 is now open! Pleae apply through the link below and send your transcript as a PDF to gsociety.rex@gmail.com (Please attach your complete transcript from ACORN as a PDF file with filename: REX2018_FISTNAME_LASTNAME).

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