Machine Learning Feature Engineering for Scientific Data

Construct a PostgreSQL framework for automated feature engineering and preprocessing of scientific datasets intended for machine learning applications. Develop stored procedures that can dynamically detect data types, handle missing values, perform automatic feature scaling, and generate statistically relevant feature transformations. Include robust error handling, support for multiple scientific domains, and performance optimization techniques for large-scale feature generation.