3 data analytics jobs found in Stuttgart

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 help to extend a process development toolbox to a wider range of fastening and assembly processes. Our goal is to leverage artificial intelligence to make the toolbox smarter and more efficient. You will test and enhance the toolbox using data from various manufacturing processes. Your task is to build an AI agent to automate and manage data integration workflows. You will also evaluate process data, identify new requirements, and contribute to the ongoing development of the toolbox. You will help us apply and expand a process development toolbox to new and exciting manufacturing processes. The...

Institute for Medical Microbiology, Medical Virology and Hygiene, code 7903 Part-time: 65% Limited: 30.11.2029* 1 Start of work: December 1, 2026 Application deadline: October 20, 2026 TV-L idR E13 2 *extension possible Tasks Development and clinical evaluation of machine-learning models for the detection of resistance plasmids directly from routine MALDI-TOF mass spectra Genomic analysis of carbapenemase-producing Enterobacterales and their mobile genetic elements, using short- and long-read whole genome sequencing Establishment and analysis of linked MALDI-TOF and genome datasets as the basis for generalizable predictive models Experimental validation of computational predictions through conjugation, plasmid curing and stability assays, supported by a dedicated technical assistant Contribution to national and international consortia (DZIF TTU-HAI, ESGEM-AMR) and presentation of results at conferences and in peer-reviewed...

Tasks Are you eager to connect physical insights with advanced AI? We are offering an exciting master's thesis opportunity to work on physically informed machine learning for system identification and performance modeling of MEMS gyroscopes. During your assignment, you will construct machine learning (ML) algorithms for system identification and performance prediction of MEMS gyroscopes. You will examine MEMS gyroscope data through detailed analysis. Furthermore, you will assess physically informed ML in comparison to other architectures. Furthermore, you will acquire a deep physical understanding of MEMS gyroscopes to optimize your models. Finally, you will work with real-world sensor data to validate your findings. profile Education:   Master studies in the field of   Informatics, Physics, Engineering or comparable with good grades Experience and Knowledge:   in data driven parameter identification; knowledge of Python,...