Benjamin Kwaku Nimako
Policy Research Fellow (Responsible Ai) @Regional Academy On The United Nations Raun
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WORK HISTORY
Policy Research Fellow (Responsible Ai) @Regional Academy On The United Nations Raun
Selected participant in the Regional Academy on the United Nations programme.Conducting policy-oriented research on Responsible Artificial Intelligence in the defence sector under the mentorship of the OSCE Representative on Freedom of the Media.Responsible for mapping international initiatives, analysing governance approaches, and contributing to policy-relevant outputs aimed at international organisations.Experience strengthened skills in systems thinking, ethical analysis, and translating technical topics for policy audiences.
EDUCATION
African Institute of Mathematical Sciences
Master's degree, Masters in Mathematical Science
Free University of Bozen-Bolzano
Doctor of Philosophy - PhD, Sustainable Development and Climate Change
Kwame Nkrumah University of Science and Technology, Kumasi
Bachelor's degree, Statistics
Istituto Universitario di Studi Superiori, Pavia
Doctor of Philosophy - PhD, Sustainable Development and Climate Change
ABOUT BENJAMIN KWAKU NIMAKO
I work at the intersection of energy systems modelling, climate analysis, and decision-relevant analytics.My background is quantitative. I began in statistics during my undergraduate studies, deepened this through mathematical sciences at master’s level, and deliberately transitioned into energy system modelling during my PhD in Sustainable Development and Climate Change. That move was intentional: I wanted to work closer to real infrastructure, real constraints, and real planning decisions.My work focuses on understanding how energy systems behave under uncertainty rather than treating models as purely predictive tools. I have worked on climate-sensitive electricity demand modelling across national and regional systems, multi-criteria decision analysis for urban energy planning, and scenario-based exploration of alternative transition pathways. Across these projects, my emphasis has been on transparency, interpretability, and usefulness for long-term planning.Technically, I work with statistical modelling, machine learning, explainable AI, and system-level tools to analyse demand dynamics, regional heterogeneity, and climate impacts. I also design reproducible code pipelines that integrate data processing, modelling, and analysis in a way that makes assumptions traceable and results easy to interrogate and update as conditions change.I am particularly interested in how modelling outputs can support robust decisions in power systems, energy policy, and infrastructure planning.I have experience working in international and interdisciplinary research environments across Europe, Latin America, and global policy programmes. I value careful thinking, clear communication, and analytical work that acknowledges uncertainty rather than hiding it.
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