The person recruited is expected to develop: (i) state-of-the-art supervised machine learning tools to predict activities of engineered metabolic pathways, (ii) generative models to design nucleotide sequences for optimization of specific enzymatic activities, and (iii) a reinforcement/ active learning pipeline to propose novel pathway sequences for synthesis and experimental characterization. These tasks will be performed using training sets built from experimental data obtained by the host laboratory. The person recruited will also benefit from several years of experience the research team has in developing machine learning for systems & synthetic biology.