3 jobs found in Reutlingen

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,...

About MATVIS MATVIS secures AI agents. They inspect traffic inline, on any model and any infrastructure, and stop prompt injection, PII leakage, data exfiltration and shadow AI before they reach anything that matters. Model-agnostic, ISO 27001 certified, and deployed inside their customers' own environments so their data never leaves. Founded in Tübingen in 2023. Enterprise customers run them in production today, most of them working against AI Act and NIS2 deadlines, so they can deploy AI with confidence. Your role As one of their two Machine Learning Leads, you will: Own core detection and analysis features end to end: dataset collection, training, evaluation, deployment, and everything that breaks afterwards. Work across the full threat surface: prompt injection, jailbreaks, PII detection and redaction, content filtering, data exfiltration, agent misuse. Build the parts of the detection stack that are not models: traffic analysis for Policy Autopilot,...

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...