DIAS professorships in Economics with a focus on applied AI and data science

This role requires expertise in economics combined with applied artificial intelligence (AI) and data science. Candidates should possess a strong foundation in economic theory and quantitative methods, alongside practical skills in AI techniques such as machine learning, statistical modeling, and data analysis. Proficiency in programming languages commonly used in data science, such as Python or R, and experience with data manipulation and visualization tools are essential. The position likely demands the ability to conduct interdisciplinary research integrating economic models with AI-driven approaches to analyze complex datasets and derive actionable insights. Familiarity with big data technologies, econometrics, and advanced statistical methods will be advantageous. Candidates should demonstrate experience in applying AI and data science methodologies to real-world economic problems, contributing to both academic research and practical policy or business applications. Strong communication skills are important for disseminating research findings and collaborating across disciplines. While specific education and experience levels are not detailed, the professorship nature of the role implies a doctoral degree in economics, data science, computer science, or related fields, along with a proven track record of research and teaching in these areas. Overall, the ideal candidate will combine rigorous economic analysis with cutting-edge AI and data science expertise to advance knowledge and innovation at the intersection of these fields.