Thesis (Master): Sustainable AI for embedded sensor systems

Job Description

Mode of Employment: Limited

Use your knowledge as a springboard.

Do you like the sound of finding the smartest solution side by side with professionals and experts? If so, complete your master’s thesis with us. We can help you to combine knowledge, discover connections, and formulate ideas. When you join our team, you will gain an insight into a range of departments and processes. It is a chance like no other to break new ground as we head into the future of electrification, automation, and digitalization. Seize this opportunity today!

Embedded sensor systems are omnipresent in today’s industrial landscape. These systems measure, process and transmit data and enable a plethora of use cases such as predictive maintenance, optimization of manufacturing processes or smart infrastructure.

The proposed thesis lies broadly in the field of optimizing the sustainability of embedded AI solutions with respect to runtime and memory footprint. The scope of the thesis is to bring an existing resource model into industrial applications and create a framework which allows to find optimal AI solutions regarding runtime and memory constraints.

Change the future with us.

  • You will implement machine learning algorithms for embedded sensor systems
  • You optimize machine learning algorithms for resource-constrained devices with a focus on memory consumption and runtime
  • Also, you obtain test and training data for industrial use cases such as classification, anomaly detection, or regression
  • You will develop a framework to optimize the sustainability of AI algorithms
  • Last but not least, you document the results

What you need to make real what matters.

  • Your ticket to join us is an ongoing study in the area of engineering or computer science at master's level with solid programming skills and experience with integrated sensors
  • You have solid experience with embedded systems, especially ARM Cortex-M processors
  • You have knowledge of machine learning methods for time series data and knowledge of neural architecture search approaches is also a plus
  • You have extensive knowledge of C/C++ for embedded systems, Python (especially machine learning and data science libraries) and MATLAB knowledge is a plus
  • You have practical thinking and willingness to try new things and explore new technologies
  • Your fluency in German or English rounds out your profile (the thesis can be written in English or German)

Make your mark in our exciting world at Siemens.
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As an equal-opportunity employer we are happy to consider applications from individuals with disabilities.


Organization: Technology

Company: Siemens AG

Experience Level: Student (Not Yet Graduated)

Full / Part time: Either

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