Bioengineering

This course covers the high throughput nucleotide sequencing technologies (ChIP-seq, DNA-seq, RNA-seq, single cell), epigenetics, circulating DNA, and mutational load. Topics also include cancer molecular databases, data extraction and analysis, and an introduction to the use of NGS software and coding.

This course gives a broad overview of neural engineering concepts and principles for recording outgoing and generating ingoing neural signals. The course gives insights into existing and future neural interfaces, neural prostheses, and neurorobotics.  It covers principles and technologies of neuroengineering applications, including basic human neuroanatomy and physiology, brain stimulator, spinal cord stimulation, functional electrical stimulation (FES), neural-machine interface for motor prosthesis control, artificial vision, and auditory devices for augmented sensory perception.

The six-week summer lab research program at National Taiwan University places students in various science, engineering and social science research labs and/or projects under the supervision of faculty. Students spend approximately 30 hours per week in lab activities.

The course covers the biomechanics and mechanobiology of skeletal tissues (bone, articular cartilage, tendons, and ligaments); the pathomechanics of injury, adaption, and degenerative changes with aging; how biomaterials can be used in loaded regions of the body; and an insight into current biomechanical research of skeletal tissues.

This course provides a theoretical introduction to microscopy, with an emphasis on fluorescence microscopy. Topics include theoretical principles of confocal microscopy and the use of deconvolution in microscopy, an overview of different types of advanced research microscopes as well as imaging methods that are not based on optical microscopes; preparation and optimization of both fixed and live samples for microscopy; microscopic visualization of cellular structures and physiological functions with fluorescent markers; and a theoretical introduction to digital visualization, with an emphasis on fluorescence-based methods and digital imaging. A practical project includes the preparation, documentation, and analysis of microscopy specimens with an oral and written presentation.

The course provides students with a fundamental grounding in the theoretical and computational skills required to apply machine learning tools to real-world problems. It will provide an understanding of the application of these skills to explore complex high-dimensional data sets; providing an overview of active research areas in machine learning, with biomedical applications.