Internship for Thesis Advanced Data Analytics for Functional Safety Validation

Let’s turn visions into reality. At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our areas of activity are every bit as diverse as our outstanding Bosch-teams around the world. Their creativity is the key to innovation through connected living, mobility or industry. Our responsibility goes far beyond ‘business’. We’re independent of stock markets and bound to the purpose of the Robert Bosch Foundation; our success directly benefits society, the environment, and future generations. 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. Let’s  be  remarkable! Welcome to a place where your ideas lead to something big. Welcome to Bosch. You will join the Bosch Application team in Turin, working in a dynamic automotive environment on advanced engine control systems. Supported by experienced engineers, you will gain hands-on experience in data analysis applied to real vehicle development projects, contributing to innovation in Functional Safety validation. You will work with real measurement data (vehicle and test bench) and advanced tools, with the opportunity to transform raw data into structured insights supporting safety validation processes. Thesis Objectives The goal of the thesis is to develop a data-driven methodology for Functional Safety validation of Engine Control Units (ECUs) , leveraging advanced data analytics and automated reporting tools (e.g. EATB). The main activities include: Define a methodology to structure and label data analysis results from vehicle validation, ensuring traceability to Functional Safety requirements Perform root-cause analysis of availability and robustness issues identified during vehicle testing Develop KPI-based metrics and data-driven validation criteria (e.g. adaptive thresholds, signal envelopes) to assess safety performance Build a data-driven decision framework to automatically classify safety requirements (validated / not validated / uncertain) based on real measurement data Analyze large datasets to identify hidden patterns and critical safety-relevant scenarios via machine learning techniques Design a scalable data storage concept to enable reuse of analysis results and support statistical monitoring. Related Activities During the thesis, you will also: Gain understanding of ECU software and diesel engine control systems Study Functional Safety functions (ISO 26262 context) Collaborate with Bosch experts across different domains Contribute to tool development for data analysis and reporting Analyze existing validation procedures and propose improvements based on data-driven insights What you will learn (value for the candidate) How to apply data analytics in real automotive development projects How Functional Safety validation is performed using real-world data How to move from raw data to: KPIs thresholds engineering decisions Exposure to industry tools such as INCA, MATLAB/Python Experience in system-level thinking and cross-domain engineering Education: Degree in Engineering (Computer Engineering, Mechatronic, Automotive, or similar) Languages: English (min. B2), Italian (min. B2) Technical skills: Strong Programming skills (Python, MATLAB) Basic knowledge of databases (SQL / NoSQL) Experience & interest in data analysis, machine learning and automotive systems Soft skills: Structured and analytical mindset Curiosity and interest in innovation Teamwork and communication skillscaaa — Published by Bosch. Apply on the employer's own posting.