A New Era in Energy Grids: Join the Revolution as a Student Assistant

In the heart of Munich, a pioneering institution is shaping the future of software-intensive systems and services. With a robust team passionate about advancing cyber-physical systems like the Internet of Things (IoT), this non-profit entity is at the forefront of technological innovation. Leveraging cutting-edge methods and tools in software development and service engineering, their mission focuses on enhancing the reliability, security, and functionality of critical infrastructures.

Be Part of the Innovation: Student Assistant in Energy Grids and Machine Learning

The evolution of energy distribution systems has been nothing short of revolutionary. As we embrace the complexities and potential of modern energy networks, we find ourselves at the precipice of a digital transformation, fueled by the advent of smart grids. These advanced networks herald a new age of extensive data interchange, propelled by an array of measuring devices scattered across the grid.

This role is a unique opportunity to dive into the development of sophisticated digital twin and data-driven models specifically crafted for energy grid applications. The project’s goal is ambitious yet critical: to automate the process of fault detection and diagnosis in low and medium-voltage grids effectively. This includes the enhancement of load forecasting capabilities, employing advanced machine learning techniques to sift through vast data sets for accurate, real-time fault localization and analysis.

Your Mission, Should You Choose to Accept

  • Advance the development of digital twins for energy grids, utilizing tools like Matlab/Simulink/Simscape.
  • Employ cloud computing, along with containerization and virtualization techniques, to scale up simulations and model training.
  • Utilize scripting languages such as Python and Bash to streamline processes.
  • Innovate in neural network models for predictive analytics, fault detection, and pinpointing issues within the grid.
  • Enhance code maintenance and issue tracking through proficient use of Git.
  • Explore and possibly pioneer new machine learning models and methodologies.
  • Engage in networking tasks and potentially contribute to research publications and knowledge sharing.

Who Are We Looking For?

  • Students engaged in Computer Science, Electrical Engineering, or related fields, either at Bachelor’s or Master’s level.
  • Individuals with experience in software development and a willingness to explore Linux environments.
  • Candidates with a foundational understanding of neural networks and machine learning, eager to apply this knowledge in practical scenarios.
  • Self-starters with a structured approach to work and excellent communication skills in English. German skills are a plus but not mandatory.
  • Applicants must be ready to work on-site, embracing the challenges and collaborations that come with this exciting role.

What We Offer

We provide an international and dynamic working environment where innovation thrives. Our team values flexibility, offering schedules that accommodate academic commitments alongside a supportive atmosphere conducive to research. This position not only opens the door to investigating fascinating topics within machine learning and digital twins but also offers the potential to further academic pursuits through Bachelor’s or Master’s thesis projects.

Take the Leap

Are you ready to contribute to groundbreaking research in energy grid digitalization and machine learning? We invite you to join our team by submitting your application. Please include a motivational statement, a detailed CV, and a current transcript of records. Grab this chance to shape the future of energy grids and embark on a fulfilling career path that blends academic pursuits with real-world impact.

Embark on this transformative journey and be at the heart of innovation in energy grid technologies. Your vision, expertise, and passion can drive the digital revolution forward, paving the way for a sustainable and efficient energy future.

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