EEG-Based BCI Research Associate

The University of Texas at Austin; Austin (TX); On-site; USD 42,322 - 51,727; Full time

Job summary

The University of Texas at Austin is seeking a Research Associate II to advance EEG-based BCI research in human neurophysiology. You will design and conduct experiments, analyze complex data, develop protocols, and collaborate with multidisciplinary teams to push scientific discovery forward.

Responsibilities include EEG data collection, data analysis with ML and statistical methods, and contributing to manuscripts and publications.

Qualifications

• Bachelor's degree in Electrical and Computer Engineering, Biomedical Engineering, Neuroscience, or related field.
• Minimum of two (2) years of relevant laboratory experience.
• Experience supporting scientific research activities in a laboratory setting.
• Preferred: Master's degree and 3+ years EEG-based BCI research, plus ML and statistical analysis experience.

Responsibilities

• Design and independently conduct EEG-based brain-computer interface experiments.
• Collect EEG and other physiological data and maintain detailed records.
• Analyze data using signal processing, ML, and statistics; prepare reports.
• Develop and optimize BCI research protocols; assist with IRB submissions.
• Collaborate with multidisciplinary research teams and contribute to publications.

Skills

EEG-based BCIs

Signal processing

Machine learning

Statistical analysis

Scientific research methods

Technical documentation

Education

Bachelor's degree in Electrical and Computer Engineering, Biomedical Engineering, Neuroscience, or related field

Master's degree in related field (preferred)

Job description

The University of Texas at Austin is seeking a Research Associate II to advance EEG-based BCI research in human neurophysiology. You will design and conduct experiments, analyze complex data, develop protocols, and collaborate with multidisciplinary teams to push scientific discovery forward.

Responsibilities include EEG data collection, data analysis with ML and statistical methods, and contributing to manuscripts and publications.