three people looking at a computer together

PhD in Epidemiology

In this program, you'll gain advanced skills in analytical methods, biostatistics, and field research methods. In addition, you’ll learn about grant writing and research ethics and have the opportunity to select a minor course of study. Our department has a strong base of funded research projects providing students with many opportunities for research support and data for dissertation projects. Our faculty are studying everything from food safety, to diabetes, to gene-environment interactions, which means you can find the mentor who’s right for you. We also have institutional training grant opportunities with our CHAMP T32 program for those interested in high dimensional data handling and analysis

Quick facts, careers, and skills

When you leave this program, you’ll be able to rigorously investigate the genetic, behavioral, environmental, and physiological factors that underlie complex human diseases and effective preventive measures.

Quick facts

Program location: CU Anschutz
Credit hours: 68
Est. time to complete: 5-7 years

Sample careers

Staff scientist
Principal investigator
Instructor
& more

Skills you'll gain

Grant writing
Study design
Data analysis and interpretation
& more

Curriculum

In this program, you’ll take courses in epidemiology, biostatistics, research methods, analytical methods, and research ethics. You'll also complete a dissertation based on work completed under the supervision of one of our world-class researchers.

Course requirement	Course ID	Credits
Biostatistics Methods I	BIOS 6611	3
Biostatistics Methods II	BIOS 6612	3
		6

Course requirement	Course ID	Credits
Research Methods with Secondary Datasets Sources	EPID 7605	3
Epidemiologic Field Methods	EPID 7911	3
Developing a Research Grant	EPID 7912	3
Analytical Methods in Epidemiology *		4
		13

*A minimum of 4 credits of advanced analytic coursework in biostatistics or epidemiologic methods from the ColoradoSPH

Course requirement	Course ID	Credits
Doctoral Thesis	EPID 8990	30

Total credits: 68

Download the information packet >

Download the program plan of study >

View the course book and course schedule >

Competencies

Identifier	Competencies	Course Where Competency is Addressed/Assessed
PHD-EPID 1	Transform scientific questions into study aims with testable hypotheses, a research protocol with appropriate data collection methods and an analysis plan.	EPID 7632 EPID 7605 EPID 7912
PHD-EPID 2	Synthesize a body of evidence, while critically evaluating methodologic quality of individual studies to identify areas of need for future investigation.	EPID 7632 EPID 7912
PHD-EPID 3	Create research proposals to answer a research or public health question using a variety of data sources; considering limitations, study design and analytic solutions.	EPID 7632 EPID 7605 EPID 7912
PHD-EPID 4	Develop statistical models appropriate to specific study designs, distinguishing between predictive, associative, and causality-based analytic approaches.	EPID 7632
PHD-EPID 5	Demonstrate mastery of a substantive area of research including knowledge and application of that knowledge in conducting original research.	EPID 7912
PHD-EPID 6	Demonstrate excellent written and oral communication skills to translate the results of research findings to the public or other professionals.	EHOH 7405

T32 Pre-Doctoral Program: Building research workforce expertise in high dimensional data analysis and communication to promote rigorous research

CHAMP: Colorado's High-Dimensional Analytics and Methods Program

The next generation of biostatisticians and epidemiologists will routinely be considering complex biomarker data. Examples include multiplex cytokine data, microbiome data, -omics and require analyses of high-dimensional, often correlated data, where the interest is in describing pathways or communities, and their influence on health. Many of these datasets will be collected in longitudinal studies as well, adding to the analytic challenge and requiring advanced training in analytical methods for describing systems rather than single exposures. These complex exposures are often best examined using tools developed in fields such as quantitative community ecology and computational biology, which are outside the realm of traditional epidemiologic and biostatistical training. 

This cross-disciplinary training program will teach trainees the tools for handling high-dimensional exposures and outcomes including ordination methods, clustering techniques, graph-theoretic approaches, data reduction strategies and network analysis.  A focus on data visualization techniques and data communication will position trainees to both analyze increasingly ubiquitous biomarker data and effectively communicate inferences to collaborators, funders and the public. Trainees will also gain skills in reproducible research to ensure rigor and transparency. Training supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Number T32GM165263.

Funding will start in September 2026. Funding is for up to 3 years, given sufficient progress and meeting of expectations.

Three year fellowship stipend is provided at the NRSA/NIH level that can be supplemented by the mentor with non-federal funds. The second and third years of funding will be contingent upon sufficient progress and meeting of expectations.

For the AY26-27 year, there will be 3 pre-doctoral slots. Pre-Doctoral Fellowships are open to current or prospective PhD students in the Epidemiology and Biostatistics Departments interested in pursuing high dimensional data management, analysis and communication skills. No single criterion will be considered a hard threshold for admittance into the training program. However, given the quantitative rigor of the program, students, through either prior undergraduate coursework or a supplementary course plan to be completed prior to entry into the training program, should meet the following eligibility criteria:

Pre-Doctoral PhD Students in their first year in an Epidemiology or Biostatistics PhD program will be prioritized for T32 funding. Students in the 2nd or 3rdyear of their programs may also be considered.
Three semesters of college level calculus (differential, integral, and multivariable), each with a grade of B or better (already a requirement of the Biostatistics PhD program).
Prior college level statistics course with a grade of B or better (already required for entry into the Biostatistics PhD program and the Epidemiology PhD program).
Program specific eligibility criteria for the respective PhD programs not mentioned above: PhD Applicants | How to apply.

Awardees are expected to devote approximately 90% of their time to research and completion of the T32 program during the first year of the award. This level of research and training time may gradually decrease over the three-year award period as the trainee progresses through the program and assumes increasing responsibilities under the guidance of their mentor.

Must be a US Citizen or Permanent Resident.

Any publication, press release, or other document about research supported by this award must include an acknowledgment of NIH award support and a disclaimer: “Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Number T32GM165263. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.”
Completion of coursework to support the competency development for high dimensional data analysis, rigorous research, and communication. The table below highlights competencies that are to be developed over the T32 training period.

Competencies for the Pre-Doctoral Training Program in High dimensional data handling and analysis

*The activities described below represent the planned program structure; specific components and implementation details are still being developed.

Skills/Competencies Developed	Course work	Timing
Apply statistical concepts of basic study designs including bias, confounding and efficiency, and identify strengths and weaknesses of experimental and observational designs. Carry out exploratory and descriptive analyses of complex data using standard statistical software and methods of data summary and visualization.	BIOS 6618 (Advanced Biostatistical Methods I)	Year 1 - Year 2
Carry out valid and efficient modeling, estimation, model checking and inference using standard statistical methods and software. Demonstrate statistical programming proficiency, good coding style and use of reproducible research principles using leading statistical software.	BIOS 6619 (Advanced Biostatistical Methods II)	Year 1- Year 2
Understand complex data structures and be able to implement strategies and methods for data management, data cleaning and assessment of measurement errors and missingness. Demonstrate proficiency in tools for analyzing complete data including ordination methods, clustering methods, latent variable methods and machine learning methods.	Complex Data Analytic Methods 1* Complex Data Analytic Methods 2* High-dimensional Data elective	Year 2-4
Demonstrate the ability to translate scientific evidence from high dimensional data projects and the uncertainty around results into comprehensible and culturally appropriate messages aimed at general audiences	EHOH 7405 (Advanced Communication Skills for Public Health Impact)	Year 2-3
Understand the importance of reproducible research and its impact on the evidence base, implement documentation best practices for process and results of the process as well as version control.	Reproducible Research Workshop	Year 2-3
Carry out independent high-dimensional data research involving implementation of complex data management, development and evaluation of novel or advanced complex data methods and their application to problems of importance in health science research, and report the methods and findings orally and in writing.	EPID 8990 or BIOS 8990	Year 3-5

Completion of Responsible Conduct of Research and Good Clinical Practice training. This includes online training, such as CITI, and face to face training.
High Dimensional Data Journal Club: Trainees are expected to participate in a regular journal club (about 6-8 hrs per month) to build knowledge of current high-dimensional data research and methods development and to promote collaboration between training grant faculty mentors and students.  Attendance of  > 80% of journal clubs is required.
Curriculum modification for training program trainees: Shifted focus on regular PhD program curriculum towards implementation and communication around high dimensional data handling and analytic methods. EPID 7605 (Research Methods with Secondary Data Sources) , EPID 7911 (Field Methods) , and EPID 7912 (Developing a Research Grant) will all require a shifted focus consistent with training in high dimensional data.
High-dimensional Data – Consulting Lab: Each trainee will participate in a consulting service offered to faculty in CSPH and SOM who have large omics or other high-dimensional datasets and need assistance or want to plan studies collecting high-dimensional exposure or outcomes data and need design and/or analytic support. This activity will provide hands-on experience and practice with various datasets and topic areas, teach skills in statistical consulting and communication, while providing a valuable service to the campus . All students will be overseen and supported by training program mentors who will review design and analytic proposals, code and results.
Immersion with High Dimensional Data Mentor: Each trainee will identify a T32 mentor who will be a liaison to the community engaged in the trainee’s area of research. The mentor will provide data and resources for the student to practice high dimensional data skills from courses.

Please create one PDF document. Required sections should be in the order listed below.

Resume/CV: Include your resume or CV detailing any experience relevant to the goals of the training program
Personal statement (approximately 1 page): Provide some background information and insight to give the reviewers an idea of who you are. Describe your mid- and long-term career goals as they relate to training in the handling, analysis, and communication of high dimensional data. Students who do not currently have the background to succeed in the program but demonstrate high potential to meet program goals can submit a supplementary course plan and will be admitted into the training program upon satisfactory completion of the prerequisites. Explain how the T32 award will augment your current training program. What is the added value of the T32 program for you?
Supplementary course plan (only for students who are applying without the required eligibility criteria): A detailed plan for how the student will obtain the necessarily prerequisite training in calculus, statistical software and/or statistics that includes proposed courses and the offering institution as well as the timing of completion.
Courses (only for students applying after at least a semester in the PhD program): List all courses completed as a doctoral student in Epidemiology or Biostatistics and grades obtained. Official transcripts are not necessary.

Application Deadline: TBD

Colorado School of Public Health

CU Anschutz

Fitzsimons Building

13001 East 17th Place

3rd Floor

Mail Stop B119

Aurora, CO 80045

Facebook Twitter YouTube Instagram LinkedIn

colorado school of public health

coloradoSPH

PhD in Epidemiology

Quick facts, careers, and skills

Curriculum

Total credits: 68

Competencies

T32 Pre-Doctoral Program: Building research workforce expertise in high dimensional data analysis and communication to promote rigorous research

Colorado School of Public Health

Course requirement	Course ID	Credits
Advanced Epidemiology 1	EPID 7631	3
Advanced Epidemiology 2	EPID 7632	3
		6

PhD in Epidemiology

Quick facts, careers, and skills

Curriculum

Core epidemiology coursework

Core biostatistics coursework

Research methods

Additional coursework

Dissertation

Total credits: 68

Competencies

T32 Pre-Doctoral Program: Building research workforce expertise in high dimensional data analysis and communication to promote rigorous research

Funding Support

Pre-Doctoral Awards (per year)

Who is eligible?

Program Requirements

Application Instructions

Funding Dates

Colorado School of Public Health