Master Thesis Opportunity in Resource-Efficient Transfer Learning for Time Series Classification

vor 2 Wochen

Erlangen, Bayern, Deutschland Siemens AG Vollzeit

Mode of Employment: Limited

Unlock Your Potential:

Are you looking for a challenging project to complete your bachelor's or master's thesis? We invite you to collaborate with our team at Siemens AG to develop innovative solutions for resource-efficient transfer learning in time series classification.

What You Will Do:

  • Explore and implement transfer learning techniques for on-site training time series classification.
  • Develop an algorithm that can adapt to new data autonomously when deployed in the field.
  • Identify the most hardware-efficient alternative combining tinyML techniques for implementation on microcontrollers or AI-accelerators.
  • Integrate the solution to create real-world impact in an industrial application, showcasing its capabilities and demonstrating on-site model updates.

What We Need:

  • 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.
  • Ideal background in machine learning, particularly in transfer learning and time series data analysis.
  • Familiarity with data preprocessing, feature extraction, and concept drift detection techniques is a plus.
  • You have the ability to integrate and test software on hardware platforms, ensuring efficient real-time performance.

Make Your Mark:

As an equal-opportunity employer, we welcome applications from diverse candidates. If you wish to learn more about Siemens AG before applying, we encourage you to explore our website.

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