Siemens Energy

Master Thesis: Enhancing Time-Series Analysis with Tabular Transformer Techniques

A Snapshot of Your Day

Take on the challenge of transforming the analysis of time-series data in the energy sector with the latest advancements in AI and machine learning. Your daily journey will include:

• Experimenting with innovative TabGPT variants to auto-encode time-series data into a tabular format.

• Conducting high-impact time-series tasks on tabular data, such as augmentation, root cause analysis, denoising, and pattern matching.

• Reversing the transformation process to recover time-series data while preserving critical characteristics.

• Collaborating with a dynamic team that values creativity and technical excellence, all in pursuit of pushing the boundaries of energy sector analytics.

How You’ll Make an Impact

•  Leverage powerful TabGPT variants to compress extensive time-series data into streamlined tabular representations, improving data processing and analysis capabilities through ground-breaking techniques.

• Innovate by developing and testing autoencoding techniques that preserve essential time-series information such as signatures, causal relationships, and noise patterns.

• Explore the use of tabular data to perform advanced analytic tasks more effectively than traditional time-series approaches.

• Contribute to the creation of a versatile tabular representation, potentially serving as a universal compression mechanism to optimize data storage and bandwidth requirements.

• Validate the efficiency of your transformation techniques using both synthetic and real sensor data, ensuring practical relevance and robustness.

• Drive improvements in time-series task performance through your transformation methods, setting new benchmarks for accuracy and efficiency within the energy sector.

By mastering the transformation of time-series data into tabular formats, your thesis work will have a profound impact on Siemens Energy's approach to time-series analysis. Your contributions will enable more efficient and effective handling of vast datasets, facilitating the development of new technologies and models. The success of your transformation methods will lead to breakthroughs in data augmentation, denoising, and pattern recognition, ultimately supporting the energy sector's push towards innovative, high-performance solutions.

What You Bring

To be successful in your application for this master thesis opportunity at Siemens Energy, you should have the following qualifications and skills:

• Currently pursuing a master’s degree in Machine learning, Statistics, Computer Science, Data Science, or a related field. Equivalent experience will also be considered.

• A solid understanding of time-series data, autoencoding processes, and transformer models.

• A passion for innovation and a desire to apply your knowledge to solve real-world problems in the energy sector.

• Strong analytical skills with the ability to think critically about data transformation and compression.

• Strong written and verbal communication skills, with the ability to detail your work and present findings effectively.

• An open approach, enthusiastic about collaborating in a diverse team and incorporating feedback from different collaborators.

About the Team 

You will get an opportunity to work with Application Platforms Business (APB) team, which is one of the teams in IT that is driving the End-to-end responsibility for all central applications and platforms 

• Solution Architecture 
• Service Management and Application Maintenance 
• Operational Provider Management 
• Digital Consulting  

Implementing all projects concerning central platforms. Orchestrating the governance structure of Business Partner Boards. You will get to know a global company in the forefront of new energy technology. During your time at Siemens Energy you will also get the opportunity to establish a network for future cooperation. 

Who is Siemens Energy?

At Siemens Energy, we are more than just an energy technology company. We meet the growing energy demand across 90+ countries while ensuring our climate is protected. With more than 99,000 dedicated employees, we not only generate electricity for over 16% of the global community, but we’re also using our technology to help protect people and the environment.

Our global team is committed to making sustainable, reliable, and affordable energy a reality by pushing the boundaries of what is possible. We uphold a 150-year legacy of innovation that encourages our search for people who will support our focus on decarbonization, new technologies, and energy transformation.

Find out how you can make a difference at Siemens Energy:https://www.siemens-energy.com/employeevideo

Our Commitment to Diversity

Lucky for us, we are not all the same. Through diversity, we generate power. We run on inclusion and our combined creative energy is fueled by over 130 nationalities. Siemens Energy celebrates character – no matter what ethnic background, gender, age, religion, identity, or disability. We energize society, all of society, and we do not discriminate based on our differences.

Application

Don’t hesitate – apply via https://jobs.siemens-energy.com/en_US/jobs , id nr 266223 no later than 2024-11-29.

Ongoing selection is applied, the role might be filled before last application date.

For questions about the role, please contact Shwetha Chandramouly on shwetha.chandramouly@siemens-energy.com

For questions about the recruitment process please contact the responsible recruiter Ermina Imamovic on ermina.imamovic.ext@siemens-energy.com

We refrain from all contact with staffing and recruitment companies, or advertising brokers.

Location: Finspång

Trade Union Representatives: 
Anders Häll, Unionen, +46122-887514 
Simon Von Eckardstein, Sveriges Ingenjörer, +46122-842 24 
Jan Lundgren, Ledarna, +46122-812 33 
Mikael Malmgren, IF Metall, +4676-6958685

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