Recent advances in biotechnology allow to generate and screen an increasing number of variants in proteins, genetic constructs and cells. However, to maximally learn from these experiments, specialized computational methods are required. This PhD project aims to develop a general-purpose toolbox to optimize biotechnological experiments based on state-of-the-art machine learning techniques.