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Interdisciplinary Biomedical Graduate Program
This program introduces students to a variety of fields through interdisciplinary courses and experimental techniques that convey knowledge of the molecular mechanisms controlling cell and tissue function in one of nine degree granting programs at the SOM.
Biomedical Informatics
The Ph.D. program in Biomedical Informatics prepares individuals for research and development careers emphasizing the application of information technology to biomedicine and health care.
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M.S. in Computational Biology
This program was established in 1999 to provide professional training to students seeking employment in the biotechnology, pharmaceutical and genomics industries. It draws on courses created for the undergraduate and graduate programs and has had a 100% success rate in job placement for its graduates to date.
Ph.D. programs in Biological Sciences, Biomedical Engineering and Computer Science
These departments (among others) have in the past recruited a number of students interested in computational biology (through the Merck Program, for example). It is anticipated that students who are better suited to, or more comfortable with, a traditional Ph.D. program will continue to apply to these programs.
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Molecular Biophysics & Structural Biology Graduate Program
Computational biology is one of the important research areas under molecular biophysics. However, the Molecular Biophysics & Structural Biology Graduate Program puts emphasis on experimental techniques and methodologies (such as X-ray diffraction, NMR, optical spectroscopy) and basic physical principles to elucidate molecular form and function of fundamental biological processes.
Program in
Neural Computation
The Program in Neural Computation (PNC) is a graduate training program in
computational neuroscience for students seeking training in the application of
quantitative approaches to the study of the brain. Specifically the program is
designed to take advantage of the world class strengths of Carnegie Mellon
University and the University of Pittsburgh in areas
including computer science, machine learning, statistics and dynamical systems
and to train students to apply these tools to critical problems in
neuroscience.
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