Welcome to the Computational Bioscience Program at the University of Colorado Anschutz Medical Campus!
The program was founded and is directed by Professor Lawrence Hunter, founder of the International Society for Computational Biology, and the popular ISMB and PSBconferences. The CPBS Program is globally recognized for its research and teaching of computational
biology and bioinformatics at the University of Colorado’s Anschutz Medical Campus. The Program is designed to produce graduates with depth in computational methods and molecular biomedicine, an intimate familiarity with the science and technology
that synthesizes the two, and the skills necessary to pioneer novel computational approaches to significant biomedical questions.
Outstanding biomedical research now requires inventive computational tools to harness the torrent of post–genomic data. Our computational innovations have led to significant insights across a broad spectrum of biomedicine. Deeper insight,
arrived at more quickly, is what medicine needs today.
Our award-winning PhD in Computational Bioscience and post-doctoral training programs create productive, interdisciplinary scientists in a relatively short period of time. Our students begin supervised research immediately, collaborating
with top scientists, working with the latest high–throughput instruments on critical biomedical problems.
Our History. The University of Colorado has a long tradition of outstanding research and training in computational bioscience. Several of the most important scientists in the field, including David Haussler and Gene Myers, received
their graduate training at the University. The School of Medicine began offering a PhD in Computational Bioscience in 2001, and was awarded the prestigious National Library of Medicine Biomedical Informatics Training Grant in 2006.
Goals of the Program
The Computational Bioscience Program of the University Of Colorado School Of Medicine is dedicated to training computational biologists who aspire to achieve excellence in research, education and service, and who will apply the skills they learn toward
improving human health and deepening our understanding of the living world. The Computational Bioscience Program provides graduates with the foundation for a lifetime of continual learning. Our curriculum integrates training in computation and biomedical
sciences with student research and teaching activities that grow increasingly independent through the course of the program. Our graduates are able to do independent computational bioscience research, to collaborate effectively with other scientists,
and to communicate their knowledge clearly to both students and the broader scientific community.
Students accepted in the PhD program are provided full tuition, health and dental insurance, and a stipend of $31,000 per year for living expenses (for the academic year 2019-2020). Continued support is contingent upon satisfactory academic and research
performance by the student. When a student enters a thesis lab, the thesis mentor assumes complete responsibility for the student’s stipend, tuition, fees, and associated research costs.
The Student Handbook
The handbook is a document and guide for current students that includes parts of the Graduate School Rules and the Computational Bioscience Graduate Program Guidelines.
Access the Student Handbook here.
The following four goals represent the foundation of the Computational Bioscience graduate education program at the University of Colorado Denver | Anschutz Medical Campus.
Educational Goals and Objectives
Knowledge Goals - Graduates demonstrate their knowledge of core concepts and principles of computational bioscience, and the ability to apply computation to gain insight into significant biomedical problems. This knowledge includes mastery of the fundamentals
of biomedicine, statistics and computer science, as well as proficiency in the integration of these fields. Graduates contribute to the discovery and dissemination of new knowledge.
- Demonstrate knowledge of the scientific principles that underlie the current understanding of molecular biology, statistics and computer science.
- Demonstrate an ability to productively integrate knowledge from disparate fields to solve problems in biomedicine using computational methods.
- Demonstrate knowledge of the types and sources of data most commonly used in computational bioscience, including knowledge of all major public data repositories.
- Demonstrate the knowledge of the classes of algorithms most often applied in computational bioscience, and their domains of applicability.
- Demonstrate an understanding of the principles and practice of the scientific method as applied in computational bioscience, including experimental design, hypothesis testing, and evaluation of computational systems.
Communication Skills Communication Skills Goals - Graduates demonstrate interpersonal, oral and written skills that enable them to interact productively with scientists from both biomedical and computational domains, to clearly communicate
the results of their work in appropriate formats, and to teach others computational bioscience skills. Graduates are able to bridge the gap between biomedical and computational cultures.
Communication Skills Objectives
- Communicate effectively, both orally and in writing, in an appropriate range of scientific formats, including formal presentations, collaborative interactions, and the critique of others’ work.
- Demonstrate familiarity with both biomedical and computational modes of expression, and be able to communicate clearly across disciplinary boundaries.
- Demonstrate commitment and skill in teaching to and learning from students, colleagues, and other members of the scientific community.
Professional Behavior Professional Behavior Goals - Graduates demonstrate the highest standards of professional integrity and exemplary behavior, as reflected by a commitment to the ethical conduct of research, continuous professional
development, and thoughtfulness regarding the broader implications of their work.
Professional Behavior Objectives
Self-Directed and Life Long Learning Skills
- Act in an ethically responsible manner, displaying integrity, honesty, and appropriate conduct at all times.
- Recognize the limits of one’s knowledge, skills, and behavior through self-reflection and seek to overcome those limits.
- Always consider the broad significance of one’s professional actions, including their implications for society and the living world.
Self-Directed and Life Long Learning Goals - Graduates demonstrate habits and skills for self-directed and life-long learning, and recognize that computational bioscience is a rapidly evolving
discipline. Our focus is on the development of adaptive, flexible and curious scientists able to comfortably assimilate new ideas and technologies during the course of their professional development.
Self-Directed and Life Long Learning Skills Objectives
1. Recognize the need to engage in lifelong learning to stay abreast of new technologies and scientific advances in multiple disciplines. 2. Locate, evaluate and assimilate relevant
new knowledge and techniques from a wide variety of sources.