Application Details

The Admissions Committee encourages students to:

1. Schedule an information with the Graduate Student Advisor to help answer initial questions and to discuss your background and interests to confirm that CPH is a good fit.
2. Research core faculty to identify those whose research aligns with your own interests. It is possible, although not mandatory, to reach out to potential faculty sponsors to ask if they are open to accepting graduate students or to arrange a lab rotation, if admitted. 

Admissions Timeline

Once the application period closes, the CPH Admissions Committee will review all applications. In early January, the committee will invite a number of students to interview for further consideration. All admissions offers will be made in early February. Students placed on a waitlist will be notified directly.

Materials Required

1. Resume / C.V. (required)

2. Statement of Purpose & Objectives (required)

   Please describe your aptitude and motivation for graduate study in your area of specialization, including your preparation for this field of study and any research experience, your academic plans and research interests, and your future career goals. Please be specific about why UC Berkeley/UCSF would be a good intellectual fit for you. 2 page maximum, single spaced.

3.  Personal History Statement (required)

   Please describe how your personal background and experiences influenced your decision to pursue a graduate degree. In this section, you may also include any relevant information on the following:

   • How you have overcome barriers to access higher education;
   • How you have come to understand the barriers faced by others;
   • Your academic service to advance equitable access to higher education for women, racial minorities and individuals from other groups that have been historically underrepresented in higher education;
   • Your research focusing on historically excluded populations or related issues of inequality;
   • Your leadership among such groups.
   • Please note that the Personal History Statement should not duplicate the Statement of Purpose. 2 page maximum, single spaced.

4. Verbal, Quantitative, and Analytical Writing Skills (required)

   Please indicate the areas of your application that highlight your verbal, quantitative, and analytical writing skills that prepare you for the specific program to which you are applying. This can be completed coursework, such as undergraduate or post-baccalaureate math/statistics or epidemiology/biostatistics courses, quantitative research, data analysis, or evaluation experience, verbal/writing strengths such as professional publications and/or presentations, etc. 250 words maximum.
   Note: Please upload this document under “supporting materials.”

5. Three Letters of Recommendation

6. Academic Transcripts (required)

Unofficial transcripts are acceptable for the application; if admitted, official transcripts are required by the Graduate Division before the time of matriculation.

   7. Relevant Coursework (required)

   8. Research Experience (optional)

   9. Writing Sample (optional)

How many students will be offered admissions?

We will welcome a cohort of 6-8 PhD students in fall 2025.

Are international students eligible for an application fee waiver?

CPH also has a limited number of application fee waivers for international students. available with an application deadline of November 20, 2024. International students can apply here. U.S. citizens or current permanent residents can apply for an application fee waiver here. 

What are the basic requirements to apply? How can I get started?

Students interested in applying are encouraged to review the Graduate Division admissions requirements and start an application for admissions. In addition, students should review the  CPH Admissions Requirements to begin the application process.

The website asks prospective students to reach out to faculty. Can we reach out to more than one professor before the application to learn about their research?

Yes! We encourage you to reach out to multiple faculty before you finalize your application. The CPH admissions application will ask you to list the top 3 faculty you are interested in working with. Reaching out to faculty does not commit you to working with them, and faculty are not expected to commit to advising you at this stage of the process. If you get in touch with faculty, this is not a formal interview–just an introductory conversation about interests. 

If admitted, what financial support will students receive? Does the same support apply to international students?

The Computational Precision Health program provides a competitive annual stipend (est. $43,800 in 2024-2025) as well as full support for tuition and fees (and supplemental non-resident tuition for international students). Students who maintain satisfactory academic progress are supported for five years (the program’s normative time to degree). The stipend level for all PhD students will be the same, regardless of the student’s home campus. 

Is there a masters degree offered during the program sequence, ie. via a comprehensive exam?  If not, do you recommend applying to the program after receiving a masters degree?

No, CPH does not offer a terminal Master’s degree during the PhD program sequence. There is no expectation or requirement to have a Masters degree before applying to our PhD.

What kind of support do CPH PhD students have in terms of post-grad career?

Students will have access to support and services from the career centers at both UC Berkeley and UCSF. CPH will hold networking events and other opportunities to connect students with potential employers. 

We anticipate that job opportunities for PhD-level computational health scientists will continue to grow, and will range from the academic professoriate, high-level research leadership in industry, clinical AI roles in health systems, and health technology innovation in the public and private sectors. As the field continues to develop, new opportunities will grow commensurately!

There is a limit to the number of programs students can apply to at UC Berkeley and UCSF, is this a different circumstance for the dual-institution program?

The CPH application is submitted through the UC Berkeley application portal; students are limited to one application per campus and can therefore not apply to another program at UC Berkeley in the same application cycle. Students may, if they are interested, apply to one program at UCSF in addition to CPH in the same application cycle.

What is the program’s acceptance rate?

Acceptance rate is about 3% and the average GPA is 4.0.

Is the program primarily looking to ask questions that are more directly relevant to the clinic or is CPH also interested in questions that are more on the biology side but could one day lead to improved treatments?

CPH is geared more towards the clinical impact end of the spectrum but also sees great opportunity for using clinical data to generate mechanistic insights that could one day lead to improved treatments. We do not draw a bright line between “discovery” and “clinical” and indeed would be very interested in projects that can do both.

How does the program envision students taking classes at both campuses? Will students be required to commute back and forth?

CPH will have a hybrid option for those courses not offered at their local/home campus. Faculty members will inform students well in advance when there may be dedicated sessions where in-person attendance is expected, such as final project presentations or special speakers. CPH wants to foster and encourage a sense of community even while hybrid. A conference room will be available at each campus for those students who prefer to attend lectures remotely and would like to do so in the same room.

How is CPH similar/different from Biomedical Informatics and Computational Biology PhD programs?

CPH is different in important ways from biomedical informatics and computational biology: 

Biomedical Informatics

The NIH defines Bioinformatics as “Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data”. Many bioinformatics programs focus only on biomedical but not clinical data (i.e., from patient care), since clinical data have stringent access restrictions to preserve privacy, are difficult to share and aggregate, and require specialized expertise (e.g., health data standards) to interpret. Biomedical informatics programs that do work with clinical data, often called clinical informatics or health informatics programs, are similar to Computational Precision Health in providing training in the specialized analysis of health data. However, the discipline of computational precision health emphasizes the combined application of computing (including machine learning and AI) and statistics to health data as described above, coupled with early and deep exposure to the clinical and public health contexts in which CPH advances are to be deployed. The result is to give students a deeper theoretical underpinning of the computational, inferential, and implementation science aspects of precision health.

Computational Biology 

Whereas Computational Biology is computation plus biology, Computational Precision Health is computation plus health and health care. The two disciplines share an emphasis on computational tools but the application areas of biology as compared to health and health care are fundamentally distinct, implying different courses of study. The applications of computational biology span far beyond health, and appropriately, in addition to computational methodologies, training in Computational Biology is focused on biology (including molecular and cellular biology, genetics, organismal biology, and population biology) and laboratory science, areas not included in core training in CPH. In contrast, clinical medicine and health care, while drawing on human biology and pathophysiology, also encompass diagnostic reasoning, therapeutic sciences, evidence-based medicine, decision making under uncertainty, implementation and behavioral sciences, health economics and health policy, patient-centered utilities, and health equity and public health.