Texas ECE offers an MSE degree in 8 different academic tracks, but students can also take advantage of the immense variety of resources for interdisciplinary work at The University of Texas at Austin. Most graduate students have the opportunity to participate in state-of-the-art research along with faculty researchers. Students who are admitted to pursue a MSE will typically require four long semesters to complete the program. A Student within the MSE program may subsequently apply to the PhD program, but there is no guarantee of acceptance.
The Chandra Family Department of Electrical and Computer Engineering currently offers three Master of Science in Engineering (MSE) programs to meet differing needs: the Traditional MSE program, the Integrated BSECE/MSE program, and the single-track Alternatively Scheduled MSE program with a concentration in Software Engineering. The latter program is offered through Texas Engineering Executive Education (TxEEE). The Traditional and Integrated BSECE/MSE programs share the same academic track advisors, the Alternatively Scheduled MSE program has its own program advisor. Students in each of these MSE programs are expected to meet the same academic standards.
Understanding, engineering, and interfacing with biological systems are among mankind's most important challenges, impacting numerous fields from basic science to health. Motivated by this larger vision, bioECE track is focused on the intersection of electrical and computer engineering with biology and medicine. It includes biomedical instrumentation, biophotonics, health informatics, bioinformatics, neural engineering, computational neuroscience, and synthetic biology. Associated faculty have expertise in diverse topics: cardiovascular instrumentation, neuroscience, neural engineering and the brain-machine interface, image and signal processing (feature extraction and diagnostic interpretation), health information technologies (data mining, electronic medical records analysis), VLSI biomedical circuits (biosensing, lab-on-a-chip), algorithms for large-scale genomic analysis, and molecular programming (engineering molecules that compute).
