Time Series Foundational Models for Electricity Price Forecasting Internship

Time Series Foundational Models for Electricity Price Forecasting – Internship

Here at Siemens, we take pride in enabling sustainable progress through technology. We do this through empowering customers by combining the real and digital worlds. Improving how we live, work, and move today and for the next generation! We know that the only way a business thrives is if our people are thriving. That’s why we always put our people first. Our global, diverse team would be happy to support you and challenge you to grow in new ways. Who knows where our shared journey will take you?

Transform the everyday with us!

Are you passionate about cutting-edge research in artificial intelligence and its application to real-world energy challenges? Do you want to develop and apply advanced time series models to predict electricity prices in dynamic markets? Here’s the right internship opportunity for you!

Join our research group, Autonomous Control and Operations, located in Princeton, NJ, for a 3 – 6 month internship, and investigate novel time series foundational models for accurate electricity price forecasting. Our close contact with different business units in Siemens provides the opportunity to contribute to and gain experience in real industrial applications, shaping the future of energy trading and grid optimization. During this internship, you will experience the excitement and challenges of industrial research at the intersection of AI and energy. An internship with Siemens Technology is a great opportunity for students to gain real-world experience in a diverse work environment.

In this role, you will work on-site in Princeton, NJ.

Our Princeton facility is recognized for providing a stimulating environment for highly talented and self-motivated students. You will have the opportunity to test your knowledge in a challenging problem-solving environment. You will be encouraged to think out of the box, innovate, and find solutions to real-life problems. Our team has a strong publication record in leading journals and conferences.

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Key responsibilities:

• Contribute to industrial software R&D projects focused on developing and applying time series foundational models for electricity price forecasting.
• Implement, evaluate, and refine state-of-the-art machine learning and deep learning models for time series prediction.
• Design and implement robust data pipelines for ingesting, cleaning, and preprocessing large-scale time series data relevant to electricity markets.
• Analyze model performance, identify areas for improvement, and contribute to the development of novel forecasting methodologies.
• Collaborate and exchange ideas with experts in the fields of machine learning, power systems, energy markets, and software architecture across Siemens research and business units.

Basic Qualifications:

• M.Sc. or PhD student pursuing a degree in Computer Science, Electrical Engineering, Applied Mathematics, Statistics, or a closely related quantitative field.
• Strong programming proficiency in Python – including extensive experience with data manipulation (e.g., Pandas, NumPy), scientific computing, and machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Demonstrated experience with time series analysis and forecasting techniques (e.g., ARIMA, Prophet, LSTMs, Transformers, state-space models).
• Hands-on experience with software development best practices: version control (Git), unit testing (pytest), and technical documentation.
• Foundational understanding of electricity markets and power systems – including market structure (e.g., day-ahead/intraday), grid operations, and factors influencing electricity prices.
• Proficiency in English, both written and verbal.
• Legally authorized to work in the United States of America and hold a valid work permit for the US for the duration of the internship?

Preferred Qualifications:

• Practical experience in research or projects involving large-scale time series data and deep learning for forecasting.
• Familiarity with foundational models or large language models (LLMs) concepts applied to time series data.
• Familiarity with transformer-based architectures and post-training of related models.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) for data processing and model deployment.
• Knowledge of energy trading, risk management, and ancillary services.
• Experience with MLOps practices for deploying and monitoring machine learning models.
• Flexibility and adaptability to work in a growing, dynamic, interdisciplinary team of experts.

A successful candidate must be able to work with controlled technology in accordance with US Export Control Law. US Export Control laws and applicable regulations govern the distribution of strategically important technology, services, and information to foreign nationals and foreign countries. Siemens may require candidates under consideration for employment opportunities to submit information regarding citizenship status to allow the organization to comply with specific US Export Control laws and regulations. Additional information on the US Export Control laws & regulations can be found on https://www.bis.doc.gov/index.php/policy-guidance/deemed-exports/deemed-exports-faqs

About Siemens:

We are a global technology company focused on industry, infrastructure, transport, and healthcare. From more resourceefficient factories, resilient supply chains, and smarter buildings and grids, to sustainable transportation as well as advanced healthcare, we create technology with purpose adding real value for customers. Learn more about Siemens here.

Our Commitment to Equity and Inclusion in our Diverse Global Workforce:

We value your unique identity and perspective. We are fully committed to providing equitable opportunities and building a workplace that reflects the diversity of society, while ensuring that we attract the best talent based on qualifications, skills, and experiences. We welcome you to bring your authentic self and transform the everyday with us.

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$32-$47