The applicant will support the research process through data preparation, exploratory on-chain analysis, feature construction, implementation of selected empirical procedures, and clear documentation of workflows and findings. This may include assembling and cleaning large blockchain datasets, summarizing transaction and wallet activity, preparing inputs for computational classification or text-based analysis, generating tables and figures, and maintaining code in a reproducible and well-organized form. The project is best suited for a student with strong quantitative ability, solid programming skills, and the patience to work carefully through large, messy datasets. Applicants should be comfortable with empirical work in Python or a similar language, and ideally have some exposure to statistics, machine learning, or data science methods. Prior familiarity with crypto is helpful but not necessary; more important is the ability to learn quickly, follow technical research procedures closely, exercise good judgment in data work, and engage seriously with both the computational and conceptual dimensions of the project.