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Thesis (Master): Beyond LLMs: Rethinking Foundation Models for Time Series Forecasting
Siemens —Erlangen
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Thesis (Master): Beyond LLMs: Rethinking Foundation Models for Time Series Forecasting

Siemens -
Erlangen

Jetzt bewerben

Details zur Stelle

Teilzeit | Vollzeit | Befristeter Vertrag
vor 10 Tagen

Qualifikationen

Informatik
Forschungserfahrung
Master
Machine Learning
Deep learning
Python
Data Science

Vollständige Stellenbeschreibung

Mode of Employment: Fixed Term

Are you ready to challenge the boundaries of AI and shape the future of time series forecasting? Join us in Munich or Erlangen as a Master Thesis student and dive deep into innovative foundation model architectures beyond LLMs—your research could redefine how we understand and predict complex systems.

What we offer you

Exciting research and development projects that put your theoretical knowledge into practice
Individual supervision and support from experienced experts in your field
Access to the latest technologies, laboratories, and resources
Diverse opportunities to contribute your ideas and actively shape the projects
Excellent career opportunities through contact with potential employers

You'll make an impact by

You apply your academic knowledge to the systematic literature review of structural differences between language and time series data within Transformer-based architectures
Building on this, you critically assess which Transformer components—such as tokenization, positional encoding, and attention mechanisms—can be successfully transferred from LLMs to time series modeling and which require adaptation
Subsequently, you conduct a targeted practical investigation of a specific finding, for example by analyzing and redesigning components like embedding layers or positional encodings, or by exploring fine-tuning strategies for time series foundation models
To validate your hypotheses, you design and execute experiments that evaluate the impact of the identified architectural modifications
Where appropriate, you extend your research to the energy systems domain, using real-world datasets from ongoing projects to test the applicability of your results

This is how you'll win us over

Education: You are currently enrolled in a Master's program in computer science, data science, electrical engineering, mathematics, or a related field with strong academic performance and are currently looking for an interesting thesis topic starting no earlier than the beginning of August
Experience and Skills:
- You have a solid foundation in machine learning and deep learning
- You are familiar with Transformer architectures and their components
- You possess programming proficiency in Python and deep learning frameworks such as PyTorch
Ways of Working: You demonstrate pro-activeness, independent thinking, and a willingness to engage with open research questions
Languages: Very good English skills are required

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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Thesis (Master): Beyond LLMs: Rethinking Foundation Models for Time Series Forecasting

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