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Mandatory Internship – Data Science and Deep Learning for Energy Systems

Siemensvor 11 StundenPraktikum
Vor OrtEnglisch erforderlichTechKI, ML & Data Science

Erforderliche Skills

Data ScienceTime Series ModelingForecastingDeep LearningMachine LearningStatistical ModelsPythonTransformerPyTorch

Stellenbeschreibung

Siemens hat diese Anzeige veröffentlicht. Wir haben unten unseren eigenen Werkstudenten-Kontext ergänzt — was diese Stelle für deine Wochenstunden, dein Netto und dein Studierendenvisum als Studierende:r in Munich, Deutschland bedeutet.

Beschreibung bereitgestellt von Siemens

Mode of Employment: Fixed Term; 35 hours / week

Ready to shape the future of energy with cutting-edge AI? Join us in Munich or Erlangen as a Mandatory Intern in Data Science and Deep Learning for Energy Systems (f/m/d) and turn your passion for forecasting, modeling, and experimentation into real-world impact!

What We Offer You

  • Exciting insights into various business areas and fields of activity
  • Challenging and practical project tasks where you can apply and expand your knowledge
  • Individual support and mentoring from experienced mentors
  • The opportunity to actively contribute to ongoing projects

You'll make an impact by

  • You support the team by conducting a comprehensive literature review on state-of-the-art forecasting methods for energy systems, including classical statistical models and advanced deep learning approaches
  • Building on this, you investigate and compare multivariate forecasting frameworks, developing strategies to integrate heterogeneous covariate information such as weather, topology, or operational signals
  • Subsequently, you explore and experiment with Time Series Foundation Models, implementing and benchmarking different forecasting models on real-world energy datasets
  • Finally, you design and execute structured experiments to evaluate forecasting accuracy, assess covariate relevance, and analyze model robustness and interpretability within the context of grid operations

This Is How You'll Win Us Over

  • Education: You are currently successfully enrolled in a master's program or in the final year of a bachelor's program in electrical engineering, energy engineering, computer science, data science, industrial engineering, or a related field, and a mandatory internship is part of your curriculum
  • Experience and Skills:
    • Solid foundation in machine learning and deep learning
    • Proficiency in Python and deep learning frameworks such as PyTorch
    • Experience with time series data or modeling and familiarity with Transformer architectures or energy systems is beneficial
  • Ways of Working: You are proactive, think independently, and are eager to engage with applied research questions
  • Languages: Very good English skills
You are much more than your qualifications, and we believe in the potential of every single candidate. We look forward to getting to know you!

At Siemens, we believe that feeling valued and included is the foundation for doing great work. That’s why we aim to create an inclusive workplace where everyone feels a sense of belonging, and where individual perspectives and experiences are celebrated. Our commitment to fairness and respect extends to every applicant.

As an equal opportunity employer, we welcome applications from individuals of all backgrounds and particularly encourage applications from persons with disabilities.

About Us

The world never stands still. And new challenges arise every day. With a passion for questioning things, for supplying ideas, and intelligently driving things forward we are helping society move towards a smarter tomorrow. Be it with technologies that reduce carbon emissions in cities or hyperintelligent robots. This is how we are able, to tackle the most important projects and push them forward together. Help us shape the future.

www.siemens.de/careers – wenn du mehr Informationen zu Jobs & Karriere bei Siemens erhalten möchtest.

FAQ – if you need further information on the application process.

If you have any further questions, please feel free to contact us: www.siemens.de/fragenzurbewerbung

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