Master Thesis - Intelligent Data Pipelines for Hydrogen Energy Materials

Your Job:

We are offering a Master’s thesis/project opportunity to join AIMWORKS, a Helmholtz-funded initiative aiming to bring advanced AI and knowledge graph solutions to sustainable hydrogen technologies. In AIMWORKS, next-generation AI agents (including large language models) streamline data ingestion, integrate ontologies, and build specialized knowledge graphs to enable robust FAIR data management. As an MSc student, you will focus on creating and refining data pipelines that automate the extraction and organization of materials science information, helping to transform cutting-edge research into actionable insights.

Your specific tasks in different areas are:

Data Integration & Ontology Development

• Assist in structuring textual, tabular, and imaging data for ingestion into ontology-based knowledge graphs (Neo4j).
  
• Contribute to refining and extending the EMMO ontology to better represent hydrogen-related materials data.
  

AI & Machine Learning Tools

• Explore modern frameworks (e.g., LangChain, basic Python ML libraries) for automated data extraction, validation, and transformation.
  
• Experiment with large language models (LLMs) to convert scientific documents into semantic representations for knowledge graph integration.
  

Workflow Automation

• Help design modular pipelines for continuous data ingestion, ensuring data quality and FAIR compliance.
  
• Collaborate with HPC or cloud environments to handle large datasets, streamlining the entire data lifecycle.
  

Collaboration & Documentation

• Work alongside researchers at IET-3 and project partners at KIT to share findings and iterate on new ideas.
  
• Document progress through regular updates and presentations; contribute to short reports or publications when possible.

Your Profile:

Enrollment in a Master’s Program

• Fields such as Materials Science, Engineering, Physics, Computer Science, or a related domain.
  

Technical Skills

• Basic proficiency in Python and a willingness to learn new libraries (e.g., for ML or ontology work).
  
• Interest or initial experience in semantic technologies (ontologies, JSON-LD) and data management is a plus.
  

Motivation & Mindset

• Passion for sustainable energy solutions, with an emphasis on hydrogen materials.
  
• Creative problem solver who enjoys interdisciplinary research and collaborative teamwork.
  

Communication

• Proficient in English (written and spoken).
  
• Comfortable engaging with international and cross-functional teams.

Our Offer:

We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change! We support you in your work with:

Active Participation in AIMWORKS

• Contribute to a high-impact project that integrates AI and knowledge graphs to advance hydrogen technologies.
  

Cutting-Edge Research Environment

• Access to modern HPC infrastructure, AI toolkits, and an internationally recognized research community.
  

Professional Development

• Gain hands-on experience in data workflows, knowledge graph development, and AI-driven analysis—critical skills for both academia and industry.
  

Flexible MSc Thesis Structure

• Typical duration of 6–12 months aligned with Master’s project requirements.
  
• Potential to co-author research papers or present findings at conferences, depending on project progress.
  

Support & Guidance

• Supervision from experienced researchers in a highly collaborative setting.
  
• Opportunity to network with experts at FZ Jülich, KIT, and other Helmholtz centers.
  

In addition to exciting tasks and a collaborative working atmosphere at Jülich, we have a lot more to offer: https://go.fzj.de/benefits

We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us.