Research Assistant — Machine Learning & Explainable AI (XAI) - Statistics &Business Analytics

The Research Assistant (RA) will work under the supervision of Dr. Osama Sohaib and contribute to the development, implementation, and evaluation of the CultureXAI framework for precision public health in the UAE. The RA will play a key role in the technical and research activities of the project, including machine learning model development, Explainable Artificial Intelligence (XAI), open health data analytics, and fairness and bias assessment across diverse population groups. The RA will be responsible for collecting, preprocessing, integrating, and analysing publicly available health, demographic, and socioeconomic datasets; developing and evaluating predictive models for non-communicable disease (NCD) risk; and applying XAI techniques, including SHAP, LIME, and counterfactual analysis, to generate interpretable insights into population-specific risk factors. The RA will contribute to academic research and dissemination activities, including literature reviews, experimental design, technical documentation, research reports, conference papers, and high-quality journal publications, particularly targeting Q1-ranked journals. In addition, the role will also involve supporting the development and evaluation of a prototype decision-support dashboard to communicate analytical findings to public health authorities, policymakers, and other healthcare stakeholders. The successful candidate is expected to demonstrate strong analytical, programming, problem-solving, and academic research skills, with the ability to work independently and collaboratively within a multidisciplinary research environment. Grade: 19.

Master's degree (MSc) in Computer Science, Artificial Intelligence, Data Science, Machine Learning, Information Systems, Statistics, or a closely related discipline. Preferred qualifications: PhD in a relevant discipline, particularly with research experience in Machine Learning, Explainable AI, Health Informatics, or Applied AI.

Develop, implement, and evaluate the CultureXAI framework for precision public health in the UAE. Conduct machine learning model development, Explainable AI (XAI), open health data analytics, and fairness and bias assessment across diverse population groups. Collect, preprocess, integrate, and analyze publicly available health, demographic, and socioeconomic datasets. Develop and evaluate predictive models for non-communicable disease (NCD) risk. Apply XAI techniques (SHAP, LIME, counterfactual analysis) to generate interpretable insights into population-specific risk factors. Contribute to academic research and dissemination (literature reviews, experimental design, technical documentation, research reports, conference papers, journal publications). Support the development and evaluation of a prototype decision-support dashboard for public health authorities and stakeholders.