Machine Learning Engineer for Time Series Classification

Vor 2 Tagen

Erlangen, Bayern, Deutschland Siemens AG Vollzeit

Unlock Your Potential with Siemens AG

We are seeking a highly motivated and skilled Master's student to join our team and work on a thesis project that combines knowledge, innovation, and real-world impact.

About the Project

As a Machine Learning Engineer, you will explore and implement transfer learning techniques for on-site training time series classification. Your task will be to develop an algorithm that can adapt to new data autonomously when deployed in the field. Additionally, you will identify the most hardware-efficient alternative combining tinyML techniques for the implementation on microcontrollers or AI-accelerators.

Your Responsibilities

  • Design and implement transfer learning algorithms for time series classification
  • Develop a solution that can adapt to new data autonomously
  • Identify the most hardware-efficient alternative using tinyML techniques
  • Integrate the solution to create real-world impact in an industrial application

What We Offer

As a member of our team, you will have the opportunity to work on a challenging project that combines machine learning, software development, and hardware implementation. You will be part of a dynamic and innovative team that is shaping the future of electrification, automation, and digitalization.

Requirements

  • Proficiency in Python for developing machine learning models and implementing algorithms
  • Experience in C++ for implementing efficient and robust software solutions on embedded systems or AI accelerators
  • 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
  • Ability to integrate and test software on hardware platforms, ensuring efficient real-time performance

Join Siemens AG and Shape the Future

We are an equal-opportunity employer and welcome applications from diverse candidates. If you are interested in this opportunity, please submit your application.

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