CALL FOR APPLICATIONS FOR PHD DEGREE FELLOWS IN NCDs RELATED RESEARCH

The Makerere University Non-Communicable Disease (MAK-NCD) Research Training Program is a research capacity building program based at Makerere University College of Health Sciences with collaboration at John Hopkins University and funded by the United States National Institute of Health-Fogarty International Centre (D43TW011401). The overall goal of this training program is to develop a comprehensive mentored research-training program in Uganda that will build local capacity to address the challenges of NCD control and management and promote the use of research findings to inform decision-making and policy.

This call for applications is targeting mainly junior researchers with interest in building an independent research career in noncommunicable diseases research with focus on Epidemiology, Data Science and Implementation Science research to strengthen evidence-based interventions, policy and control in Uganda

Fellowship package

The PhD fellowship support is for up to three (3) years (full-time), subject to annual performance reviews and progress milestones. Successful scholars will be registered at Makerere University and may undertake sponsored didactic research methods training at the Johns Hopkins University (JHU), USA, to receive additional skills development and mentorship in NCD research.

PHD SCHOLARSHIP

  1. Must have a concept or a research idea proposal in one of the following NCD areas; Chronic Respiratory Diseases (CRD) with focus on Chronic Obstructive Pulmonary Disease and Asthma, Chronic Kidney Disease (CKD), cardiovascular diseases (CVD) & Hypertension.
  2. Concepts for studies that identify and validate key biomarkers and demonstrate robust machine‑learning methodologies for predicting risk, supporting diagnosis, characterizing disease severity, assessing treatment response, and estimating prognosis for the targeted NCDs.
  3. Studies that leverage multimodal data sources, including genetic, clinical, demographic, and environmental datasets or benchmark machine‑learning models against established risk‑scoring systems.
  4. Studies that aim to use existing genetic datasets related to kidney disease; identify key genetic variants linked to CKD onset and progression; and develop machine‑learning models to predict CKD risk using genetic, epidemiological and clinical data.
  5. Commitment to develop and maintain a productive career devoted to NCD
  6. Must have two (2) mentors in the proposed area of research.

Application Resources:

The following datasets are available for candidates who aim to use large data sets:

  • Vicinity cohort RHSP (~2,400 Echocardiography measurement done, ~3,000 spirometry, ~20,000 blood pressure measurements)
  • Longitudinal Rakai Community Cohort Survey, South Central Uganda 20 surveys conducted since 1994
  • Fatumo, Segun, et al. "Uganda Genome Resource: a rich research database for genomic studies of communicable and non-communicable diseases in Africa." Cell Genomics 2.11 (2022).

Details in the attached flyer