Data Science | AI Engineering internship: Stochastic optimization for value-driven Predictive Maintenance

Job description

Introduction This internship is hosted within Predictive Maintenance & Diagnostics (Development & Engineering, FC‑26), closely connected to TC‑0001 Applied Data Science. Within FC-26, teams develop data‑driven methods to predict failures, optimize maintenance timing, and quantify the economic value of predictive models. A key challenge is moving beyond simple downtime metrics toward holistic cost and value evaluation, including yield impact and fleet‑level interactions. This internship focuses on developing a multi‑modal maintenance cost model and a stochastic optimization framework to evaluate predictive maintenance strategies. Your assignment: During this internship, you will: Develop a layered cost model, starting from system‑level SD/USD costs and extending to yield degradation and cluster‑level effects. Incorporate The assignment is connected to existing ASML maintenance-simulation tooling but is explicitly framed as a research thesis, not an IT or product development task. It offers a strong combination of academic depth and real industrial relevance, with exposure to advanced topics in reliability engineering and stochastic optimization, as well as the opportunity to work on...