Internship modeling the physical dynamics of micro-electro-mechanical systems

Internship Modeling the Physical Dynamics of Micro-Electro-Mechanical Systems Tübinger Straße 123, 72762 Reutlingen, Germany Full-time Robert Bosch GmbH Company Description At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference. The Robert Bosch GmbH is looking forward to your application! Job Description In this internship you will work on a challenging, high-impact problem at the intersection of physics, engineering, and machine learning: understanding and modeling the causal, physical dynamics of Micro-Electro-Mechanical Systems (MEMS). MEMS are sensor systems containing micrometer-sized mechanical structures combined with electronics. They are among the most prevalent sensor systems today due to their functionality and conveniently small size, and are widely used in everyday devices such as smartphones, laptops, and cars. This internship targets highly motivated students with strong academic background who want to gain experience in applying their physics intuition and abstract thinking to real industrial systems. The manufacturing of the micrometer-sized structures is a multi-stage process of high dimensionality and is susceptible to disturbances and contamination. This complexity makes controlled adaptation, optimization, and root-cause analysis time-intensive and often necessitates different approaches. A causal model, that is, a model that explains the causal relationships between system variables via a set of equations and an associated graph structure, can solve the former objectives more efficiently. The goal of this work is for you to iteratively construct a causal model for a production subprocess. For this purpose, you will first learn the fundamentals of causality and MEMS devices through provided literature. You will then use your acquired knowledge of the physical dynamics and interactions, as well as their mathematical description to derive the system equations from physical principles and represent them in a graph structure, iteratively validating your assumptions with domain experts. After successful model construction, you benchmark popular machine learning algorithms that aim to infer causal relationships from data. Finally, you summarize your work and findings in a report. The model construction requires a deep understanding of the physical dynamics of MEMS and their manufacturing, as well as their mathematical description (e.g. differential equations). During the internship you will develop a deep understanding of MEMS and causality, strengthen your modeling skills and gain experience working on open-ended industrial research problems. Qualifications Education: master studies in the field of Physics, Electrical Engineering, Mechanical Engineering or comparable with good gardes Experience and Knowledge: solid understanding of Physics, Statistics, Probability Theory and Differential Equations, good understanding of MEMS, basic Python skills, basic SQL skills Personality and Working Practice: you are open, self-driven and persistent Work Routine: your on-site presence is required Enthusiasm: fascination with physical dynamics, as well as their mathematical description; comfortable working on open-ended problems without predefined solutions Languages: good communication skills in written and spoken English and German Additional Information Start: according to prior agreement Duration: 3 - 6 months Requirement for this internship is the enrollment at university. Please attach your CV, transcript of records, enrollment certificate, examination regulations and if indicated a valid work and residence permit. Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity. Need further information about the job? Anna Hunstig (Functional Department) 49 7121 35 18315 Work LikeABosch starts here: Apply now! LI-DNI