Thesis (Master): Covariate-Enriched Netload Forecasting for Power System Operations Using Time Series Foundation Models **Mode of Employment:** Fixed Term Ready to shape the future of energy? Join us in Munich or Erlangen for your Master Thesis and dive deep into netload forecasting, leveraging time series foundation models and covariate data to revolutionize power system operations. **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 directly to real-world netload forecasting challenges- Building on this foundation, you analyze historical netload data and systematically assess the predictive relevance of covariates, including weather, topology, PV generation, redispatch, and calendar features- Subsequently, you establish baseline forecasting models as benchmarks before designing and evaluating multivariate models ranging from classical statistical approaches to advanced Time Series Foundation Models (TSFMs)- You further investigate how TSFMs, such as Chronos-2, can effectively integrate heterogeneous covariate information to improve forecasting accuracy- Finally, you conduct comparative evaluations to ensure the practical applicability and performance of your models in the context of power system operations **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 closely related field and are currently looking for an interesting thesis topic starting no earlier than the beginning of August- **Experience and Skills**:- You possess a solid foundation in machine learning and statistical modeling- You have programming proficiency in Python, with experience in libraries such as pandas, scikit-learn, or PyTorch- You are able to work with real-world, potentially noisy and incomplete datasets - **Ways of Working**: You demonstrate pro-activeness and a strong willingness to engage with applied research in an industrial context- **Languages**: You have 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](https://www.siemens.com/de/de/unternehmen/jobs/arbeiten-bei-siemens/inklusion.html), 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](https://www.siemens.com/en-us/company/jobs/) – wenn du mehr Informationen zu Jobs & Karriere bei Siemens erhalten möchtest. [FAQ ](https://www.siemens.com/en-us/company/jobs/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](http://www.siemens.com/fragenzurbewerbung) #sagthesis