Optimize model inference to run efficiently on our edge-infrastructure (Raspberry Pi 4). Incorporate MLOps practices to automate and streamline model deployment. Share your ML expertise and implement state-of-the-art ML best practices
Design an agent prototype that interacts with the environment - Design dashboard prototypes to properly communicate necessary changes in resource allocation - Technologien und Skills … Knowledge of machine learning algorithms reinforcement learning)
Als Teil unseres Teams gestaltest du innovative Bildverarbeitungskomponenten für unser iriis-System. Du fokussierst dich auf die Produktentwicklung und bist für die Systemarchitektur/Interfaces unserer ML-Platform verantwortlich
What we offer you - Automotive companies are intensively developing automated driving functions and vehicles. Unfortunately, we have already seen severe road accidents involving automated vehicles … Performance comparison of the Machine Learning methods developed at AVL
What we offer you - Fitting Physical Models to the test measurements of the Batteries or Fuel Cells are a powerful tool in capturing their inner characteristics. However, the fidelity of the physical model is highly dependent on the set of physical phenomena coved by mathematical formalism
Partner Call open until: October 2025 - Project start: Q1 2026 - Distributed learning for process optimization … Objectives … The Embedded Systems Research Division is actively seeking collaboration for a project focused on developing innovative methods and algorithms for decentralized analysis of online data streams
Develop in-depth knowledge of acoustic emission analysis and related AI/ML techniques … Qualified degree (MSc) in mechanical engineering, electrical engineering, physics or similar - Good analytical skills combined with problem-solving orientation
Development of new generation machine learning methods focused on the detection of object, lane and driving scenarios - Integration of the developed methods into AVL platforms - Testing and analysing full self-driving stacks under different hardware and software configurations with the integrated methods
Unfortunately, such testing with a full factorial variation of the parameters is not feasible. In AVL, we are developing methods for the efficient identification of critical scenarios. Performance comparison of the Machine Learning methods developed at AVL
Development of machine learning methods - Implementation of the developed methods into AVL testing pipeline of Batteries and Fuel Cells - Overcoming limitations of sparse and out-of-distribution training datasets … Ongoing studies in the fields of Computer Science, Telematics, Physics or Electrical Engineering