Master of Science in Biomedical Engineering - Bioinformatics

The overall objective of the School of Biomedical Engineering, Science and Health Systems is to provide multidisciplinary programs offering an instructional core curriculum and research in selected areas. The core requirements for the master's in Biomedical engineering include a minimum of 45 course credits (most courses carry three credits each) and an optional research thesis. While a research thesis is highly recommended a Non-Thesis option is also available. Students who elect to pursue a Non-thesis option are required to complete a minimum of 51 credits of coursework to be approved by the School Graduate Advisor. Students admitted into the biomedical engineering program are individuals who have earned undergraduate degrees in one of the traditional engineering areas. Students with undergraduate degrees in computer science; physics; chemistry; bio-chemistry, or mathematics may qualify for admission into the graduate biomedical science program. The core curriculum provides the necessary training in medical science, modeling and simulation and biomedical engineering applications. Students may focus their scholarly efforts on advanced coursework and research in such areas as Biomedical Imaging, Biomedical Instrumentation, Biomechanics, Biomaterials, Human Performance, Biomedical Signals, Neuroengineering, and Tissue Engineering. While such concentrations are facilitated, the School does not offer formal certification in these sub-areas and the final degree is MS in Biomedical Engineering. Bioinformatics This specialization emphasized a systems engineering approach to provide a foundation in systems biology and pathology informatics. Students are provided students with hands-on experience in the application of genomic, proteomic, and other large-scale information to biomedical engineering as well as experience in advanced computational methods used in systems biology: pathway and circuitry, feedback and control, cellular automata, sets of partial differential equations, stochastic analysis, and biostatistics.