Mode of Employment: Fixed Term 

Are you ready to revolutionize wireless communication in industrial environments? Join us as a Master Thesis student at MCH GCH, diving deep into "Site-Specific SNR Distribution Estimation Using Gaussian Mixture Models". Embark on a journey where your research will pave the way for next-generation network configurations and quality of service in dynamic factory settings.

What we offer you

• Exciting research and development projects that put your theoretical knowledge into practice
• Individual supervision and support from experienced experts in your field
• Access to the latest technologies, laboratories, and resources
• Diverse opportunities to contribute your ideas and actively shape the projects
• Excellent career opportunities through contact with potential employers

You'll make an impact by

• You will initially create a detailed 3D model of the laboratory environment using Blender or similar software, which includes the dielectric properties of objects to accurately simulate wireless signal behavior
• Subsequently, you will employ the Sionna ray-tracing tool to compute the channel impulse response (CIR) from the 3D model, aiding in the accurate estimation of power delay and phase delay profiles
• Using Gaussian Mixture Models (GMM), you will analyze the CIR data to approximate the Signal-to-Noise Ratio (SNR) distribution and the envelope of wireless channel propagation within the factory environment
• You will set up a simulation grid to calculate CIR for all possible transmitter-receiver pairs in your environment, deriving block error rates (BLER) using a physical abstraction model from Sionna or related frameworks
• Finally, you will validate your findings on real test beds using Universal Software Radio Peripheral (USRP) devices, confirming the accuracy of your power delay profile and SNR distribution under various testing scenarios

This is how you'll win us over

• Education: You are currently successfully enrolled in a master's program in Electrical Engineering, Informatics, or a related study program with a background in wireless communication
• Experience and Skills:
  • Proficiency in Blender or similar software for 3D model design and experience with Sionna or USRPs is advantageous
  • Strong analytical and mathematical skills, particularly in optimization and machine learning methods, including Gaussian mixture models
  • Solid programming skills in Python and C++
• Ways of Working: You exhibit self-initiative and the ability to work independently, especially when solving complex problems
• Languages: Excellent English skills, both written and spoken, with strong scientific writing capabilities

You are much more than your qualifications, and we believe in the potential of every single candidate. We look forward to getting to know you!

Your individual personality and perspective are important to us. We create a working environment that reflects the diversity of the society and support you in your personal and professional development. Let’s get to know your authentic personality and create a better future together with us. As an equal-opportunity employer we are happy to consider applications from individuals with disabilities.

The world never stands still. And new challenges arise every day. With a passion for questioning things, for supplying ideas, and intelligently driving things forward we are helping society move towards a smarter tomorrow. Be it with technologies that reduce carbon emissions in cities or hyperintelligent robots. This is how we are able, to tackle the most important projects and push them forward together. Help us shape the future.

www.siemens.de/careers – if you would like to find out more about jobs & careers at Siemens.

FAQ – if you need further information on the application process.

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