Hello, I am John Paul Shinggu, a computational chemist and educator with a passion for applying quantum chemistry and molecular modelling to astrochemistry and drug discovery. I hold a BSc in Industrial Chemistry and an MSc in Physical Chemistry from Federal University Wukari and am pursuing a PhD in Computational Chemistry at IIT, focusing on protein signaling pathways. My work blends software tools, data analysis, and scientific writing to advance understanding of molecular systems while mentoring students and collaborating on interdisciplinary projects.
I enjoy exploring novel computational methods, contributing to research publications, and communicating complex ideas to diverse audiences. I am actively building expertise across hardware and software tools for both research and teaching, and I welcome opportunities to collaborate on challenging problems in chemistry, data science, and science communication.
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The project focused on developing highly challenging and unambiguous prompts designed to expose gaps and weaknesses in AI model reasoning. By creating questions that even advanced models struggled to solve, the goal was to identify specific reasoning errors and improve the models’ analytical accuracy, contextual understanding, and overall problem-solving capabilities. An example of a prompt I once used was:
Which surface characterization technique would be most appropriate for rapidly determining the relative concentrations of carbon and oxygen contaminants on a large set of 30 silicon wafers, with analysis confined to the top few atomic layers and assuming the relative sensitivities for carbon and oxygen are known?
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