Thesis (Master) Resource-Efficient Transfer Learning for Times Series Classification

vor 1 Monat

Erlangen, Deutschland Siemens AG Vollzeit


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 bachelor’s or 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

What part will you play?

You will explore and implement transfer learning techniques for on-site training time series classification. You will develop an algorithm that can adapt to new data autonomously when deployed in the field. Besides, you will identify the most hardware efficient alternative combining tinyML techniques for the implementation on microcontrollers or AI-accelerators. Moreover, you will integrate the soluntion to create real world impact in a industrial application, showcasing its capabilities and demostranting on-site model updates.

What you need to make real what matters.

You are proficient in Python for developing machine learning models and implementing algorithms. You have experience in C++ for implementing efficient and robust software solutions on embedded systems or AI accelerators. Ideally you have strong background in machine learning, particularly in transfer learning and time series data analysis. Familiarity with data preprocessing, feature extraction, and concept drift detection techniques are a plus. You have an ability to integrate and test software on hardware platforms, ensuring efficient real-time performance.

Make your mark in our exciting world at Siemens.

if you wish to find out more about Siemens before applying.

As an equal-opportunity employer we are happy to consider .

#sagthesis

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