SCHOOL OF MEDICINE
SCHOOL OF MEDICINE
Ph.D. in Biomedical Sciences
Campus: Astana, Kazakhstan, NU School of Medicine building
Language: English
Delivery mode: Full-time, on-campus
Duration: 4 years
Total ECTS credit: 240
General information
The Ph.D. in Omics in Health Sciences (PhDOHS) is a four-year, full-time collaborative doctoral program jointly offered by Nazarbayev University (NU) and the University of South Florida (USF). This innovative program prepares students to become leaders in computational biology, omics and genomics data science, and translational bioinformatics. The program is delivered in a hybrid, on-campus mode within the NU School of Medicine and requires a total of 240 ECTS credits.
A unique feature of this program is joint supervision of doctoral theses by NU and USF faculty, with an option for a one-year residency at USF. This offers students access to international research settings, advanced genomic and bioinformatics facilities, and guidance from global experts.
The program aims to position graduates to address significant local and global health issues by bridging the divide between clinical care and bioinformatics, ultimately improving patient outcomes and life expectancy.
Program aims
The program aims are:
- Train students to conduct cutting-edge research integrating bioinformatics, computational biology, and multi-omics analytics to address significant challenges in communicable and non-communicable diseases.
- Develop capacity to analyze and interpret complex omics datasets using advanced biostatistical and bioinformatics approaches.
- Provide training in grant writing, scientific communication, research ethics, and interdisciplinary collaboration through jointly taught NU-USF courses and international research exposure.
Curriculum
Overall 240 ECTS
- Courses: 24 ECTS
- Thesis research: 216 ECTS
The curriculum covers a broad range of topics required for omics in health science research:
- PHDOHS-701 – Molecular Basis of Genetic Disease
- PHDOHS-702 – Fundamentals of Omics
- PHDOHS-703 – Inferential Biostatistics
- PHDOHS-704 – Writing for Biomedical Sciences
- PhDOHS 706 – Thesis Research
Course descriptions
This course provides a foundation in classical statistical inference for the biomedical and health sciences. Students will learn to draw valid conclusions from sample data. The curriculum covers core inferential methods, including point and interval estimation, confidence intervals, and hypothesis testing. Students will apply standard statistical tests, such as t-tests, ANOVA, chi-square tests, correlation, and simple linear regression, equipping them with the practical tools to analyze, interpret, and report clinical and biological data.
This course highlights key characteristics of science writing and emphasizes the importance of organizing writing around research outcomes, communicating difficult discipline and statistical concepts clearly and effectively for scientific and general audiences. Focused on the writing stages of planning, drafting, and revising texts, this course teaches through engagement with scientific literature and practical examples and exercises. Students will analyze and produce common scientific writing genres while considering the rhetorical, ethical, and technical aspects of developing and producing texts. This course will also prepare students to write their interim and final PhD theses. Topics include effective writing principles, writing process, grant writing, peer review, ethics in science writing, and writing for general audiences.
This course, running through Years 1- 4, emphasizes high-level execution, analysis, and communication of students' primary research. Students will apply research skills through core experiments and complex analyses. They will learn to generate, interpret, and independently troubleshoot original research findings. The course will also prepare students thoroughly to defend their dissertations in a formal oral examination, demonstrating comprehensive mastery of their research field and methodology. In addition to the dissertation itself, the course emphasizes the professional dissemination of the work, where students prepare the research manuscript for submission to peer-reviewed journals.
Program learning outcomes
On successful completion of the program, graduates will be able to:
- Formulate and execute hypothesis-driven research applying bioinformatics, molecular, and analytical methods to omics data.
- Analyze and interpret multi-omics and biomedical data using bioinformatics and statistical frameworks.
- Employ molecular epidemiology and phylogenetic approaches to investigate the evolution and dynamics of human diseases.
- Employ bioinformatics, biophysical, and machine learning approaches for drug and vaccine design.
- Critically evaluate ethical principles, data governance, and policy frameworks related to biomedical data generation, sharing, and application.
- Prepare and present research proposals, publications, and conference outputs demonstrating clarity, rigor, and leadership in interdisciplinary settings.
Career opportunities
Graduates of the program will be prepared to become globally competent scholars and leaders. By emphasizing data-driven health insights, students are equipped to improve evidence-based practices.
Possible future career paths include roles in:
- Academia, industry, and government sectors.
- Research and development focused on computational biology and multi-omics.
- Translational bioinformatics, tackling complex health challenges through data-driven discovery and innovation.