About this role
About the position A three-year position as Postdoctoral Research Fellow in machine learning and artificial intelligence in epidemiology is available at the Department of Public Health and Interdisciplinary Health Science, Institute of Health and Society, Faculty of Medicine, University of Oslo. The position is linked to the project LINDA-FAMILIA – Implementation of an Integrated Digital Health System for Infectious Diseases in Maternal and Child Health in East Africa, a Horizon Europe/Global Health EDCTP3 project. The postdoctoral fellow will contribute to a work package on clinical research co-led by the University of Oslo and the Uganda National Institute of Public Health. Up to 10% of the position will be devoted to career-promoting work, primarily teaching and supervision in machine learning and artificial intelligence for master students in epidemiology. About the project: LINDA-FAMILIA will do research within digital eRegistries for reproductive, maternal, newborn and child health services in four regions in East Africa: Addis Ababa Region, Ethiopia; Eastern Province, Rwanda; Kilimanjaro Region, Tanzania; and Lango sub-Region, Uganda. The eRegistries are deployed to replace paper-based health information systems and support clinical care, disease surveillance and research through harmonized longitudinal individual-level data across the four countries. The systems include clinical decision support, referral coordination, targeted client communication, data management and interoperability. The project will demonstrate the scientific value of these routinely collected real-world eRegistry data for multi-country clinical and epidemiological research on poverty-related infectious diseases in maternal, perinatal, neonatal and child health. More about the position The postdoctoral fellow will work at the interface of epidemiology, causal inference, prediction modelling, responsible AI, digital health and global maternal and child health. The work will include development and application of machine learning and AI methods to large-scale, longitudinal, routinely collected eRegistry data. The successful candidate will collaborate with researchers, PhD candidates, postdoctoral fellows, public health institutions and Ministries of Health in Ethiopia, Rwanda, Tanzania and Uganda, as well as partners in Europe. Some international travel for project meetings, workshops and collaboration with country teams should be expected. A career plan shall be developed for the Postdoctoral Fellow, specifying the competencies the Postdoctoral Fellow should acquire. UiO is responsible for following up on the career plan and ensuring that the Postdoctoral Fellow has access to career guidance throughout the postdoctoral term. Up to 10% of the position will be devoted to career-promoting work, primarily teaching and supervision in machine learning and artificial intelligence for master students in epidemiology. The duration of appointment is 3 years. Your areas of responsibility will be The successful candidate will: Develop and apply machine learning and AI methods for epidemiological research using longitudinal eRegistry data. Contribute to comparative epidemiological studies across countries and data systems. Apply causal inference and targeted learning methods, including TMLE and Super Learner approaches where relevant. Contribute to the development and validation of risk prediction models for severe maternal, perinatal, neonatal and child outcomes related to poverty-related infectious diseases. Contribute to geospatial epidemiology analyses using GIS-linked eRegistry data. Develop reproducible R-based analysis pipelines, including support for DataSHIELD or other privacy-preserving/distributed analyses. Contribute to data harmonization, data quality assessment, missing data strategies, data anonymization and data sharing procedures. Contribute to protocols, statistical analysis plans and reporting for the registry-based cluster randomized trial of SMS and automated voice messaging reminders. Collaborate with and support researchers in the partner countries, including training and capacity-building activities. Publish results in peer-reviewed journals and present findings at international conferences and project meetings. Contribute to teaching and supervision in machine learning and AI for master students. Qualifications You must have : A degree equivalent to a Norwegian doctoral degree in epidemiology, biostatistics, statistics, machine learning, artificial intelligence, computer science, health data science, public health, medicine with strong quantitative methods, or a closely related field. The doctoral dissertation must be submitted for evaluation by the application deadline. Appointment is dependent on the public defense of the doctoral thesis being approved before the start of employment. Documented competence in statistical modelling, machine learning, artificial intelligence, causal inference, prediction modelling or related quantitative methods. Experience with analysis of large, complex health data, such as longitudinal data, registry data, electronic health records, cohort data or trial data. Strong programming skills in R, Python or equivalent scientific computing languages. Strong R skills are particularly relevant for this project. Excellent written and oral communication skills in English. Desired qualifications: Experience with one or more of the following will be considered an advantage: Maternal, perinatal, neonatal or child health epidemiology. Infectious disease epidemiology, poverty-related diseases or global health research in low- and middle-income countries. Digital health, DHIS2, eRegistries, electronic health records or routine health information systems. Targeted learning, TMLE, Super Learner, ensemble methods, causal machine learning or related methods. Geospatial epidemiology and GIS methods. DataSHIELD, federated learning or distributed multi-country data analysis.