Sobre el papel
Ubicación
Alemania
Nordrhein-Westfalen
Muelheim an der Ruhr
- País/Región: Germany
- Estado/Provincia/Condado: Land Berlin
- Ciudad: Berlin
- País/Región: Germany
- Estado/Provincia/Condado: Bayern
- Ciudad: Erlangen
Remoto u Oficina
Híbrido (Remoto/ Oficina)
Empresa
Siemens Energy Global GmbH & Co. KG
Organización
SE CFO
Unidad empresarial
Gas Services
Tiempo completo/parcial
Jornada completa
Nivel de experiencia
Estudiante
Location: MLH R/ BLN H/ ERL S
Mode of Employment: Full-time / Fixed Term
A Snapshot of Your Day
This master thesis is hence supposed to investigate the capabilities of Knowledge Graphs in combination with Large Language Models.
Large Language Models (LLMs) have become more and more relevant within the last years and especially after OpenAI’s release of the GPT model series. In general, these ready-to-use models come with a very broad knowledge which allows them to respond correctly to a wide range of questions. When it comes to domain or company-specific knowledge, however, it is not possible to retrieve detailed information from these black-box-models without additional methods. One approach extending the capabilities of LLMs involves the generation and utilization of so-called Knowledge-Graphs (KGs) that contain domain-specific content. These KGs, in turn, can be used for Fine-Tuning or Retrieval Augmented Generation (RAG). This master thesis is hence supposed to investigate the capabilities of Knowledge Graphs in combination with Large Language Models.
How You’ll Make an Impact
- Literature Research on Knowledge Graphs regarding advantages, disadvantages, general use cases, architectural design, best practices, existing open-source libraries, etc.
- Literature Research on Large Language Models in combination with Knowledge Graphs
- Creation of Knowledge-Graphs using internal and external data related to the field of Finance
- Identification and implementation of the most promising approach improving the capabilities of Large Language Models in combination with created Knowledge Graphs
- Validation and presentation of results
- Documentation by means of a master thesis
What You Bring
- Master studies in the field of Computer Science or Informatics
- Fluent in English, German is a plus
- Strong practical experience with Python
- Strong knowledge in Machine Learning and Language Models
- Experience with Latex & MS Office
- Experience with Snowflake is a plus
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 94,000 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.
Rewards/Benefits
- Exciting insights into an international company
- Lay the foundation for your career with us
We value equal opportunities and welcome applications from people with disabilities.
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