PhD in Computer Science, Information Systems, Data Science, Machine Learning, Applied Mathematics, Quantitative Economics, or closely related fields. Strong background in network science, graph analytics and simulation. Solid foundation in machine learning, statistics, and mathematical modelling. Strong expertise in R or Python and good familiarity with scientific computing libraries. Working knowledge of explainable AI techniques (e.g., SHAP, LIME, counterfactual explanations). Strong written and oral communication skills, with peer-reviewed publications.

Shortlisting Online or in-person interviews