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 fully funded PhD fellowships that will provide tuition and a stipend for a duration of upto 3 years.
• Spend upto 3 months at UCSF, USA in a data science related research lab with a paid economy class return ticket, accommodation and stipend for stay at UCSF.
• Institutional IRB fees for PhD project.
• UNCST fees for PhD project.
• Open Access Publication fees for up to 2 publications.
• 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; and d) Natural Language Processing (NLP) and (or) generative AI solutions for health problems.

Submission
Submit the following documents as a single pdf file
1. Certified copies of relevant academic documents.
2. Two reference letters from academic referees.
3. Statement of motivation (max 1500 words).
4. Your idea for the research project (single page).
5. An updated CV (max 4 pages).

• This is a full-time PhD fellowship. It is expected that the intending applicant is not involved in any other form of study or employment.
• Only successful candidates will be notified.