The new Graduate Certificate in Computational Neuroscience will help bridge the gap between experimentally focused studies and quantitative modeling and analysis, giving graduate students a chance to broaden their skill sets in the diversifying field of brain science.
“The broad, practical training provided in this certificate program will help prepare both quantitatively focused and lab-based students for the increasingly cross-disciplinary job market in neuroscience,” said Victoria Booth, professor of mathematics and associate professor of anesthesiology, who will oversee the program.
more information
-
Enrollment is not yet open, but information sessions will be scheduled for early next year. Register for the program’s mailing list.
To earn the certificate, students will be required to take core computational neuroscience courses and cross-disciplinary courses outside of their home departments, participate in a specialized interdisciplinary journal club, and complete a practicum.
Cross-discplinary courses will depend on a student’s focus: students in experimental neuroscience programs will take quantitative coursework, and students in quantitative science programs such as physics, biophysics, mathematics and engineering will take neuroscience coursework.
The certificate was approved this fall, and will be jointly administered by the Neuroscience Graduate Program and the Michigan Institute for Computational Discovery and Engineering.
Along with the Graduate Certificate in Computational Neuroscience, U-M offers several other graduate programs aimed at training students in computational and data-intensive science, including:
• The Graduate Certificate in Computational Discovery and Engineering, which is focused on quantitative and computing techniques that can be applied broadly to all sciences.
• The Graduate Certificate in Data Science, which specializes in statistical and computational methods required to analyze large data sets.
• The Ph.D in Scientific Computing, intended for students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their doctoral studies. This degree is awarded jointly with an existing program, so that a student receives, for example, a Ph.D in aerospace engineering and scientific computing.
