PhD (Epidemiology/Clinical Trials) Fellowship-Linda Familia Project is Here. Apply Now!

PhD (Epidemiology/Clinical Trials)

Fellowship Summary

Applications are invited for a PhD Fellowship in the Horizon Europe LINDA-FAMILIA project within the Uganda National Institute of Public Health (UNIPH). This is a full-time temporary position which is available for a period of four (4) years from January 1^st, 2026. The PhD Fellow will work with a Research Fellow and a multidisciplinary team at the UNIPH on a project in Buikwe and Mukono Districts in Central Region, and Apac, and Lira City in Lango Region of Uganda.

LINDA FAMILIA Project

The LINDA-FAMILIA project is a multi-country project funded by EDCTP. It aims to implement an innovative, interoperable, and adaptable open-source digital health information system replacing paper-based systems in maternal and child health units in the following four regions in East Africa: Addis Ababa Region, Ethiopia; Eastern Province, Rwanda; Kilimanjaro, Tanzania; and Central and Lango Subregions, Uganda.

In the LINDA-FAMILIA project, we propose to adapt and scale up a digital health system across a region in each of the following four countries in Africa (Tanzania, Rwanda, Ethiopia and Uganda) to capture pregnancy, postpartum, neonatal and paediatric data including data on infectious diseases and vaccinations. The system will support maternal and child disease surveillance and health services delivery.

Objectives

The PhD fellow will follow the PhD program in any public university of choice in Uganda. He/she will work together with 3 other PhD students based at Ethiopian Public Health Institute (EPHI) and Kilimanjaro Clinical Research Institute (KCRI) to:

• Conduct comparative epidemiology studies across countries; across current vs. eRegistries data; and across traditional regression-based analyses vs. targeted maximum likelihood estimations (TMLE) with machine learning in the Super Learner package in R.
• Conduct geospatial epidemiology studies of infectious disease distribution and spread in maternal and perinatal life (e.g. in sf and stars packages in R) leveraging the geographic information system (GIS) capabilities embedded in the eRegistries, linking care providers to healthcare facility location, and clients to their residential village.
• Conduct optimized risk prediction models for severe infectious disease outcomes with machine learning estimator selection for prediction from the full “big data” eRegistries in the Super Learner package in R.
• Conduct a cross-country cluster Randomized Controlled Trial (c-RCT), analysed in R, of the effectiveness of SMS messages in increasing the proportion of pregnant women who receive the recommended number of antenatal visits and the children who receive the recommended vaccines based on routinely collected real-life data in the eRegisties under real-world conditions.

Tasks

Specifically the PhD student will:

a. Design and execute geospatial epidemiology

This will involve conducting studies of the distribution and spread of HIV, hepatitis B, malaria, tuberculosis (TB), syphilis and sepsis, and other poverty-related infectious diseases in maternal and perinatal life using the geographic information system (GIS) capabilities embedded in the eRegistries, linking care providers to healthcare facility location, and clients to their residential village.

b. Design and execute predictive epidemiology

This will involve conducting predictive models for severe adverse outcomes of poverty-related diseases based on the richness, granularity and volume of eRegistry data with machine learning estimator selection for prediction using the Super Learner package in R.

c. Design and execute a registry-based c-RCT.

This will involve undertaking a two-arm cluster randomized trial across the four regions to examine the effectiveness and equity impact for (Population) pregnant women of (Intervention) short messaging services (SMS) and automated voice messaging (AVM) (DHI 6: Targeted Client Communication) from the eRegistres, vs. (Comparator) no messaging activated, in increasing the (Outcome) proportion of pregnant women with timely attendance to antenatal care according to national recommendations; timely attendance for specialized care when referred from routine antenatal care due to complications in pregnancy; and facility delivery at the appropriate level based on their individual risk profile. Clusters will be randomised with stratification by district and type of healthcare facility. Pregnant women in the intervention clusters will receive messages about the time and location of their upcoming antenatal care or specialized care they have been referred to, as well as reminders of the appropriate level of healthcare facility for delivery of their baby.

Secondary outcome measures are pre-eclampsia, preterm birth, small for gestational age, birth at a healthcare facility and maternal, perinatal and neonatal mortality. We expect that women will receive 2-3 messages per visit; however, the messages and content will co-design with local health authorities.

Qualifications and personal qualities

• The successful applicant must have a masters’ degree with a minimum overall score of B. The degree must be in public health, public health informatics, epidemiology, statistics, clinical trials or other of similar relevance for the implementation of this project.
• Research experience with fieldwork in epidemiology, public health, public health informatics, clinical trials or other of similar relevance for the implementation of this project is desirable.
• Experience of project leadership with fieldwork in the public health, public health informatics or epidemiology sector in similar settings as Uganda is desirable.
• The applicant must have good communication skills both orally and in writing and be fluent in written and spoken English.
• Personal characteristics such as abilities to work independently and collaborate in a team will be emphasized. The applicant must be flexible, solution-oriented and with a strong drive to get things done. To succeed as a PhD fellow, the candidate must have excellent organizational and time-management skills, be motivated and responsible, and have a great work capacity, commitment and enthusiasm for research and dissemination.

Your application must include;

• A brief letter of application stating your motivation for the position, why you are applying and why this position is perfect for you.
• An overview of your education and work experience (CV).
• Copies of relevant certificates.
• A list of publications and academic work (max 10).
• Two references (name, contact information, and a brief description of their work-relation to you).

How to apply

Candidates should apply by sending the application by email to application@uniph.go.ug, attaching relevant documents by 21 November 2025. No late applications will be accepted.

General information

Further information about the position can be obtained from: Prof. Dr. Alex Riolexus Ario, UNIPH (phone: +256772363348 or e-mail: riolexus@uniph.go.ug)