PhD Position - AI-Enhanced Graphs Sustainable Hydrogen Technologies

Organisation/Company:
Forschungszentrum Jülich

Research Field:
All

Researcher Profile:
First Stage Researcher (R1)

Country:
Germany

Final date to receive applications: 19 Jan 2038 - 03:14 (UTC)

Type of

Contract:

To be defined

Job Status:
Other

Is the job funded through the EU Research Framework Programme? Not funded by a EU programme

Is the Job related to staff position within a Research Infrastructure? No

Area of research: Promotion

Your Job:

We invite applications for a PhD candidate to contribute to AIMWORKS, a Helmholtz-funded research initiative devoted to developing AI-enhanced knowledge graphs for materials science in hydrogen technologies. The AIMWORKS project leverages advanced AI agents—including large language models (LLMs) and Retrieval Augmented Generation (RAG)—to automate data ingestion and semantic enrichment across complex, multimodal datasets. By integrating established ontologies such as EMMO with graph databases (Neo4j), this project aims to elevate data interoperability and foster collaborative, FAIR data practices throughout the Helmholtz Association.

Your specific tasks in the different areas:

• Develop methods for mapping heterogeneous data (text, images, tabular results) into an ontology-based knowledge graph using EMMO and Neo4j.
• Employ LLM-based approaches to extract and structure metadata, ensuring seamless semantic interoperability for hydrogen technology research.

• Contribute to the design and implementation of agentic workflows for data collection, preprocessing, and iterative refinement of knowledge graphs.
• Work with modern AI frameworks (e.g., Lang Chain, PyTorch, Tensor Flow) to automate information extraction and annotation across large scientific datasets.

• Define protocols for metadata standards in alignment with FAIR principles (Findable, Accessible, Interoperable, Reusable).
• Establish automated pipelines for data ingestion, validation, and semantic annotation, collaborating with HPC services for large-scale analytics.

• Apply developed AI/ML models to optimize materials design, characterizing essential properties and performance metrics across the hydrogen value chain.
• Deploy advanced LLMs for Retrieval Augmented Generation, feeding relevant scientific content into knowledge graph construction and refinement.
• Work closely with project partners at FZJülich (IET-3), KIT, and beyond.
• Present results at international conferences, co-author high-impact publications, and actively participate in the AIMWORKS consortium’s collaborative ecosystem.

Your Profile:

• Master’s degree in Physics, Chemistry, Materials Science, Computer Science, or related fields.

• Proficiency in Python.
• Familiarity with semantic technologies and graph databases.
• Basic understanding of large language models (LLMs) and AI-based data extraction methods.

• Proven problem-solving and analytical skills, with a passion for interdisciplinary collaboration.
• Interest in bridging fundamental research and digital integration for sustainable hydrogen solutions.

• Excellent English language skills (spoken and written).
• Strong interpersonal competencies for effective collaboration in a dynamic, international team.

Our Offer:

• Conduct frontier research at the intersection of AI, materials science, and sustainable hydrogen technologies.

• Access to exclusive training programs (e.g., HITEC Graduate School), plus networking opportunities across Helmholtz centers and industrial partners.
• Further development of your personal strengths through an extensive range of training courses; a structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and Supervisors:

• State-of-the-art HPC resources, advanced AI toolkits, and fully equipped labs for physical and computational experimentation.

• Engage with leading experts in digital…