Professor Yousif Hassan is looking for a curious, detail-oriented, and organized individual to work as a  research assistant. The candidate will work with Professor Hassan on his project Data Governance in  Africa: Decolonizing Data Infrastructures and Imaginaries of the future, which examines the landscape of  AI and data governance in a few select African countries. The main objective of this effort is to gain greater  understanding of the issues, challenges, and opportunities surrounding the ethical and responsible use of AI  in the continent. The research assistant’s tasks will vary, from in-depth data collection and analysis to writing and administrative work including transcribing interviews and organizing project files. Experience  with tools/scripting languages such as R, Python or similar to conduct quantitative analysis of documents and generate visualizations such as maps is preferred. This is an ideal position for current undergraduate or  graduate students interested in science, technology, or public policy, and the development and regulation of  technological innovation to serve public interest and social justice goals, whether in the United States,  Europe, or in low- and middle-income countries.  

The ideal candidate will: 

• Be self-motivated and task oriented, able to execute tasks independently to move project forward

• Be persistent, and not dissuaded by the complexity or technical details of the topics

• Be able to learn technical topics and explain them simply 

• Have strong organizational skills and attention to detail 

• Be able to work independently to solve problems 

• Be able to conduct a systematic analysis of AI and data policy documents, proposals, and  strategy documents. 

• Be able to synthesize complex findings from multiple sources (including academic articles)  clearly and concisely in written form 

• Have excellent writing skills (and some familiarity with policy writing) 

• Have interest in Africa 

• Preferably, have experience with topic modeling and ML/NLP techniques for analyzing document corpora and generating spatial or network maps to visualize the relationships between documents  and their semantic content.