Join our Physical Layer Development Division in München (Germany) at the earliest possible date.
Your tasks
* AI/ML Model Optimization: You will support the optimisation our AI/ML models for digital signal processing in terms of performance and resource utilization on our low-power System-on-Chips (SoCs).
* Technology Research: You will research current trends and the possibilities of various low-power SoCs in the field of Edge AI/ML for DSP applications.
* Toolchain Evaluation: You will participate in the evaluation of various machine learning toolchains for the implementation on our SoCs.
* Performance Analysis: You will assist in estimating and comparing the maximum achievable performance through dedicated implementations and conduct performance measurements on selected SoCs for AI/ML-based DSP tasks.
Your qualifications
* You are an enrolled student in computer science, informatics, electrical engineering, telecommunications or comparable field of studies
* You have a basic understanding of digital signal processing and digital communication
* You have initial experience with Machine Learning and Deep Learning (AI/ML) technologies
* You are proficient in Python and Matlab – you ideally have prior experience with frameworks such as TensorFlow, TensorRT, or ONNX
* You are characterized by a structured approach to work, analytical thinking, and teamwork skills
* You have very good written and spoken German and English skills
Interested?
We are looking forward to receiving your application! The best way to apply is online using the reference number. If you have any questions, please feel free to contact your recruiting contact at any time.
You must be properly enrolled for the duration of your employment. You can work up to 20 hours per week during the lecture period. Mobile working is possible - but your main place of work is Munich.
We place great importance on equal opportunities. We look forward to receiving your application regardless of gender, nationality, ethnic and social origin, religion, ideology, disability, age as well as sexual orientation and identity.
Reference number: 15516
Recruiting contact: Emilia Kansik