Your Path to
Digital Health Innovation


Our Programs
M.Sc. in Computational Biology
Build expertise in computational biology, AI, and health data science through research-driven study.
- 2 years full-time
- 36 credits
- Core + elective courses, internship, and thesis
- Fully funded with competitive stipend
Program Structure
- 4 Core Courses (16 credits)
Foundational computational biology curriculum - 2 Elective Courses (8 credits)
Customize your learning path - Internship (2 credits)
Real-world industry experience
- Research Methods Course (2 credits)
Essential research skills - Master's Research Thesis (8 credits)
Original research contribution
Career Outcomes
- Doctoral degree programs
- Careers in biotechnology and healthcare industries
- Research positions in academia
- AI and data science roles in the life sciences sector
Ph.D. in Computational Biology
Drive innovation in digital health through advanced, original research guided by world-class faculty.
- 4 years full-time
- 60 credits
- Research-intensive with dissertation & publications
- Fully funded with competitive stipend
Program Structure
- 4 Core Courses (16 credits)
Advanced computational biology foundation - 2 Elective Courses (8 credits)
Specialized knowledge areas - Internship (2 credits)
Professional development
- Research Methods Course (2 credits)
Advanced research methodology - PhD Research Dissertation (32 credits)
Original, publishable research
Career Outcomes
- Academic research and faculty positions
- Government research institutions
- Corporate R&D leadership
- Biotechnology and pharmaceutical companies
Distinctive Features
Designed to prepare future leaders at the intersection of AI, biology, and public health through an education that blends innovation, research, and global impact.
Hands-On Learning
Gain practical experience through labs and internships
Research Integration
Engage in cutting-edge research guided by expert faculty
Industry Connections
Collaborate with leading partners in AI and biotechnology
Interdisciplinary Focus
Combine computing, data science, and life sciences to drive innovation
Introduction to Single Cell Biology and Bioinformatics, Molecular Biology for Machine Learning, Analyzing Multi-Omics Network Data, Computational Genomics and Epigenomics
Machine Learning & Deep Learning, Natural Language Processing, Medical Imaging, Big Data & Data Mining, Specialized AI for Biomedical Data, Generative AI Applications
Strong academic and quantitative background, interest in AI and digital public health, and passion for research impact.
How to Apply
Application deadline: 27 Feb 2026, 5pm (GST)
Prepare Materials
Prepare CV, transcripts, statement of purpose, and references.
Submit Application
Complete the online application portal with your documents.
Interview
Shortlisted candidates may be invited to an interview with faculty.
Admission Offers
Offers are made on a rolling basis to successful applicants.
Join the Future of Digital Public Health


