She Data Science (SHEDS)
Empowering Uganda’s Women in Health Data Science: Identifying Barriers, Bridging Knowledge and Innovation for Tangible Impact
Call open: August 19th 2025
Number of positions: 2
Application deadline: September 3rd 2025
Proposed start: October 1st 2025
The She Data Science (SHEDS) project is pleased to invite applications from eligible female candidates for MSc Research year fellowships for the year 2025. SHEDS is a collaborative initiative between the African center of Excellence in Bioinformatics and Data Intensive Sciences, Infectious Diseases Institute, Makerere University, Kampala, Uganda and the Institute of Global Health Sciences (IGHS) at the University of California San Francisco (UCSF), USA.
About the SHEDS program
The increasing adoption of technologies like mobile phones, high throughput genomic sequencing, IoT and electronic health records is accelerating the buildup of an avalanche of data: clinical, genomic, epidemiological, climate-related and social/behavioral data. These growing volumes and complexity of data, render the rapidly expanding field of “Big Data†analysis and interpretation essential to improving health and economic outcomes.
Data Science (DS), which encapsulates Machine Learning and Artificial Intelligence (AI) provides a pathway to the leveraging and enhancement of these data into meaningful and actionable information. However, the highly technical nature of DS as well its powerful potential, simultaneously pause the risk of ‘leaving behind’ sections of the population that have already been disadvantaged. In Uganda in particular, the high gender disparity within STEM fields means that women are more likely to be left behind, resulting in the unintended consequence of DS further widening the gender gap in STEM.
This program is thus going to target the training and advancement of Ugandan women in data science and (or) bioinformatics. It achieves this goal through three critical areas: 1) Skilling women in data science / bioinformatics methods and techniques, 2) identify barriers to women in STEM, Data Science and bioinformatics 3) Providing a bridge to help trainees translate their data science skills into biomedical and public health practice.
Benefits of the program
- There are two fellowships that will provide a stipend to two MSc students during the research year.
- Research clearance fees for MSc student project.
- World class health data science mentorship from some of the best mentors in the field.
Research concept
The research concept should be one that employs data science, mathematical and (or) bioinformatics to any of the following areas:
a) Antimicrobial resistance (AMR) including the design of antimicrobial drug combination therapies, identifying One Health AMR transmission pathways, and utilizing data science methodologies to guide antimicrobial stewardship initiatives
b) Human Genomics including the role of repeats in the human genome
c) cancer including cancer genomics and genomics data science
d) Natural Language Processing (NLP) and (or) generative AI solutions for health problems.
Submission
Submit the following documents as a single pdf file to the IDI application portal
- CLEAR copies of certified relevant academic documents.
- A one-page concept note of your proposed research
- Two reference letters from academic referees
- Statement of motivation (max 1000 words)
- An updated CV (max 4 pages)
NB:
- This is a full-time MSc scholarship. It is expected that the intending applicant is not involved in any other form of study or employment.
- Only successful candidates will be notified.
- This opportunity is targeted towards females
Key Responsibilities
- Fully develop a research proposal that employs data science, mathematical and (or) bioinformatics to any of the following areas:
a) Antimicrobial resistance (AMR) including the design of antimicrobial drug combination therapies, identifying One Health AMR transmission pathways, and utilizing data science methodologies to guide antimicrobial stewardship initiatives
b) Human Genomics including the role of repeats in the human genome
c) cancer including cancer genomics and genomics data science
d) Natural Language Processing (NLP) and (or) generative AI solutions for health problems.
2. Successfully defend your research proposal
3. Successfully obtain ethics approval from Institutional Review Board (IRB) and National approval from Uganda National Council for Science & Technology (UNCST).
4. Present your work at scientific workshops, conferences and research forum
5. Succesfully defend your Masters thesis
6. Write at least 1 peer reviewed scientific publications in high impact journals.
7. Participate in ACE research groups relevant to your area of study.
Academic Qualifications
- Bachelor's of Science in Biochemistry
- Bachelor's of Biomedical Laboratory Technology
- Bachelor's of Biotechnology
- Bachelor's of Science in Computer Science
- Bachelor's of Biomedical Sciences
Person Specification
Eligibility
- Minimum qualifications: Applicants should have completed a bachelor’s degree in any of the following STEM fields: Bachelor's of Science in Biochemistry, Bachelor's of Biomedical Laboratory Technology, Bachelor's of Science in Computer Science, Bachelor's of Science in Biotechnology or a related field from a recognized university
- Be enrolled for an MSc in Bioinformatics and data science at Makerere University.
- Have completed the first year of taught modules evidenced by a stamped testimonial from the department hosting the MSc degree.
- A viable and innovative research concept in any of the above-mentioned research areas.
- Should have data for their research concept
- Demonstrated interest in any of the following areas :
a) Antimicrobial resistance (AMR) including the design of antimicrobial drug combination therapies, identifying One Health AMR transmission pathways, and utilizing data science methodologies to guide antimicrobial stewardship initiatives
b) Human Genomics including the role of repeats in the human genome
c) cancer including cancer genomics and genomics data science
d) Natural Language Processing (NLP) and (or) generative AI solutions for health problems.
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