| Government Entity | University of the Witwatersrand, Johannesburg |
|---|---|
| Location | Gauteng, Johannesburg |
| Reference Number | 106489 |
| Centre / Location | Johannesburg,ZA |
| Closing Date | July 14, 2026 |
| Source and Application | wits.ac.za |
Organization: School of Public Health
The Wits School of Public Health (Wits SPH) is one of the largest Schools of Public Health in South Africa, with an international reputation for its teaching programmes, conducting high quality and relevant research, community outreach, and policy engagement activities.
The School is seeking to appoint two pre-PhD interns to support the creation of a unique research data resource through integration of rich, multidimensional datasets covering a broad spectrum of health domains, including infectious and noncommunicable diseases, maternal and child health, environmental health and health systems. The data will be drawn from diverse sources including disease registries, longitudinal population platforms (health and demographic surveillance systems), surveys, clinical and laboratory investigations, and geospatial platforms.
Successful candidates will enjoy unparallel opportunities for career development and professional growth including support for PhD studies.
Key Performance Areas
· Implement record linkage of health and demographic surveillance systems (HDSS), clinical and laboratory data with National Cancer Registry data.
· Perform analyses of linked data
· Contribute to peer-reviewed publications
Required Minimum Education and Training
Ideal candidate would possess a Master?s degree in Applied Mathematics/Statistics/ Epidemiology/ Demography/ Data Science or other highly quantitative and computational fields. Candidates in the final stages of completing Master?s degrees in the relevant disciplines will also be considered.
Desirable additional education, work experience and personal abilities
- Strong research interests in data linkage, quantitative epidemiology, health data science and experience in statistical and computational analyses of large complex longitudinal datasets
- Ability to code in one or more scientific programming languages (e.g., R, Python, C/C++)
· Proficiency in SQL.
· Experience in writing research reports and manuscripts for peer-reviewed publications.
· Demonstrated knowledge and track-record of statistics and proficiency using a statistical software package (STATA, SAS, R) particularly for import, manipulation, and analysis of large datasets.
Remuneration
The remuneration package offered will depend on qualifications and experience and is subject to the University of the Witwatersrand criteria.
For technical enquires please contact [email protected]
Requirements
Ideal candidate would possess a Master?s degree in Applied Mathematics/Statistics/ Epidemiology/ Demography/ Data Science or other highly quantitative and computational fields. Candidates in the final stages of completing Master?s degrees in the relevant disciplines will also be considered. Desirable additional education, work experience and personal abilities Strong research interests in data linkage, quantitative epidemiology, health data science and experience in statistical and computational analyses of large complex longitudinal datasets Ability to code in one or more scientific programming languages (e.g., R, Python, C/C++) Proficiency in SQL. Experience in writing research reports and manuscripts for peer-reviewed publications. Demonstrated knowledge and track-record of statistics and proficiency using a statistical software package (STATA, SAS, R) particularly for import, manipulation, and analysis of large datasets. Remuneration The remuneration package offered will depend on qualifications and experience and is subject to the University of the Witwatersrand criteria. For technical enquires please contact [email protected]
Duties
Implement record linkage of health and demographic surveillance systems (HDSS), clinical and laboratory data with National Cancer Registry data. Perform analyses of linked data Contribute to peer-reviewed publications
Source / Circular Reference
wits.ac.za