2025-2026 Academic Catalog

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Computational Bioscience (PhD)

Admissions Requirements

To apply for admission applicants must submit the following:

  • Online application
    • Personal Statement: A roughly one-page personal statement describing the applicant’s career goals and purpose for seeking a Computational Bioscience PhD
    • Resume: The applicant’s current resume or curriculum vitae, including professional work/practice since graduating with a bachelor’s degree (or equivalent).
    • Past Work Statement
    • Three recommendations: to be completed by people who know your professional, academic and/or personal achievements or qualities well. As such, references must be from professional contacts, such as employers, supervisors, former faculty, preceptors, or professional colleagues. References from clergy, family members, friends or politicians will not be accepted.
  • Application Fee: A nonrefundable application fee of $50.00 (U.S. dollars) for domestic applicants. Checks or money orders should be made out to the University of Colorado.
  • Interview: After the application is complete a telephone or video interview will be arranged with the applicant and around 6 faculty members. This interview will afford the program the opportunity to understand the needs of the applicant and for the candidate to ask questions. The interview process is designed to assess the applicant’s knowledge of the profession, communication, and ability to perform in a positive, professional manner when working with others. To be considered for admission, applicants must participate in the interview process.
  • Transcripts: Official transcripts from all post-secondary colleges and/or universities should be sent directly to:

University of Colorado Denver
Graduate School
Mail Stop C296
Fitzsimons Building, C5000
13001 E. 17th Place
Aurora, CO 80045

OR Electronic Transcripts should be sent to: graduate.school@cuanschutz.edu

International students must meet ALL of the requirements above and those required by International Admissions; additionally, their application fee is $75 U.S. Dollars.

Degree Requirements

First Year

Plan of Study Grid
Year 1
FallHours
BMSC 7806 Core I: Foundations in Biomedical Sciences 6
BMSC 7810 Core Topics in Biomedical Science Core Topics A 2
BMSC 7810 Core Topics in Biomedical Science Core Topics B 2
CPBS 7601 Computing Skills in the Biomedical Sciences 2
CPBS 7602 Introduction to Big Data in the Biomedical Science 2
CPBS 7605 Ethics in Bioinformatics 1
 Hours15
Spring
CPBS 7712 Research Methods in Biomedical Informatics 4
CPBS 7605 Ethics in Bioinformatics Section 001 1
CPBS 7605 Ethics in Bioinformatics Section 0V3 1
 Hours6
Summer
CPBS 8990 Doctoral Thesis 1-10
 Hours1-10
 Total Hours22-31

Second Year

Plan of Study Grid
Year 2
FallHours
BIOS 6601 Applied Biostatistics I 3
Or take 1 of the following courses:
 
Biostatistical Methods I  
Statistical Theory I  
CPBS 7605 Ethics in Bioinformatics 1
 Hours4
Spring
CPBS 7605 Ethics in Bioinformatics 1
 Hours1
Summer
CPBS 8990 Doctoral Thesis 1-10
 Hours1-10
 Total Hours6-15

Third Year & Beyond

Plan of Study Grid
Year 3
FallHours
CPBS 7605
Ethics in Bioinformatics (Confirm with Program Administrator)
or Doctoral Thesis
1
 Hours1
Spring
CPBS 7605
Ethics in Bioinformatics (Confirm with Program Administrator)
or Doctoral Thesis
1
 Hours1
Summer
CPBS 8990 Doctoral Thesis 1-10
 Hours1-10
 Total Hours3-12

Learning Objectives

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.

Knowledge Objectives

  1. Demonstrate knowledge of the scientific principles that underlie the current understanding of molecular biology, statistics and computer science.
  2. Demonstrate an ability to productively integrate knowledge from disparate fields to solve problems in biomedicine using computational methods.
  3. Demonstrate knowledge of the types and sources of data most commonly used in computational bioscience, including knowledge of all major public data repositories.
  4. Demonstrate the knowledge of the classes of algorithms most often applied in computational bioscience, and their domains of applicability.
  5. 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

  1. 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.
  2. Demonstrate familiarity with both biomedical and computational modes of expression, and be able to communicate clearly across disciplinary boundaries.
  3. 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

  1. Act in an ethically responsible manner, displaying integrity, honesty, and appropriate conduct at all times.
  2. Recognize the limits of one’s knowledge, skills, and behavior through self-reflection and seek to overcome those limits.
  3. 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 Skills 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.

Courses

BIOS 6611 -  Biostatistical Methods I  (3 Credits)  
This first course in applied statistics covers basic descriptive methods and probability; parametric and nonparametric inference for the one- and two-sample location problem; ANOVA, ANCOVA, and multiple linear regression. Matrix notation, R, and SAS are used. Prerequesite: differential calculus or permission of instructor
Grading Basis: Letter Grade
A-PUBH BIOS
Typically Offered: Fall.
BIOS 6631 -  Statistical Theory I  (4 Credits)  
This course presents an introductory coverage of the theory of discrete and continuous random variables and applications to statistical problems. Topics include probability theory, transformations and expectations, common families of distributions, multiple random variables, and properties of a random sample. Prereq: Differential and integral calculus.
Grading Basis: Letter Grade
A-PUBH1 Graduate students and public health certificate students only.
Typically Offered: Fall.
BMSC 7806 -  Core I: Foundations in Biomedical Sciences  (6 Credits)  
Course will focus on the fundamental principles of biomedical sciences. Lectures and recitations/discussions will primarily address the basics of molecular biology, biochemistry, genetics, cell biology and energetic principles. Course is typically limited to biomedical science PhD and BSBT MS students. Previously offered as IDPT 7806
Grading Basis: Letter Grade
Repeatable. Max Credits: 6.
Typically Offered: Fall.
BMSC 7810 -  Core Topics in Biomedical Science  (2 Credits)  
Sections focus on different core topics in biomedical science, and will address subject areas such as protein structure and function, neurobiology, embryology, stem cell research, and cancer biology. Students can enroll in multiple Core Topic Courses topics in one semester. Previously offered as IDPT 7810.
Grading Basis: Letter Grade
Repeatable. Max Credits: 20.
AMC-PHD PhD Students only
Typically Offered: Fall.
CPBS 7605 -  Ethics in Bioinformatics  (1 Credit)  
Discussions of professional conduct, social implications of research and questions raised by biomedical research, with an emphasis on topics relevant to computational biologists. Active student participation is required. Offered every other year.
Grading Basis: Letter Grade
Typically Offered: Fall, Spring.
CPBS 7712 -  Research Methods in Biomedical Informatics  (4 Credits)  
This course focuses on application of algorithms to analysis of different types of big data and provides training in how to plan, develop, execute and report on research in computational biology. Topics include: 1) Molecular Data; 2) Biomedical data; 3) Drug/disease data.
Grading Basis: Letter Grade
A-GRAD Restricted to graduate students only.
Typically Offered: Spring.
CPBS 8990 -  Doctoral Thesis  (1-10 Credits)  
Doctoral Thesis work in Computational Bioscience. Prerequisites: Permission of instructor.
Grading Basis: Letter Grade with IP
Repeatable. Max Credits: 10.
A-GRAD Restricted to graduate students only.
Additional Information: Report as Full Time.
Typically Offered: Fall, Spring, Summer.

Policies

Please refer to the Graduate School Policies page.

Contact Us

Katerina Kechris, PhD
Professor
Program Director
Katerina.Kechris@cuanschutz.edu
303-724-4363

Evelin Zumba
Program Administrator
Evelin.Zumba@cuanschutz.edu
CPBS@cuanschutz.edu
303-724-3350