PhD Position in Physics (Julich Research Center)

The IAS-9 focuses on data-driven methods tailored to challenges in the physical sciences and engineering. The research group "Knowledge Engineering for Materials Science" applies semantic technologies to improve data interoperability, reuse, and reasoning in materials research. Our work focuses on using ontologies and knowledge graphs to structure materials data and embed physical meaning into datasets, while leveraging techniques such as Large Language Models (LLMs) to improve semantic enrichment. We also contribute to the development of domain ontologies, metadata standards, and software tools that enable FAIR data practices across scientific workflows. Our work aligns with Helmholtz-wide and national data initiatives including NFDI-MatWerk, and contributes to shaping a sustainable, AI-ready research data infrastructure. Your Job Join an interdisciplinary team that brings state-of-the-art AI research together with cutting-edge materials science and physics. Depending on your background, you will work collaboratively on the following tasks with either a stronger model-development or application focus: Design knowledge-graph-augmented transformers and retrieval-augmented generation (RAG) pipelines that enable semantic querying and reasoning over materials-science/physics corpora Develop pipelines for semantic enrichment of unstructured data, including entity recognition, relation extraction, and automatic ontology alignment in physics and materials domains Build and maintain ontologies, OWL/RDF knowledge graphs, SPARQL endpoints, and open benchmarking suites to guarantee FAIR, reusable research data Mine and link structure-property relationships from DFT, MD, phase-field, TEM/SEM, and other multimodal datasets from simulation and experiment Develop benchmarking protocols and toolkits to evaluate AI models on materials science tasks, and integrate semantic-AI services into high-throughput GPU/HPC workflows, contributing to data management, metadata structuring, and semantic annotation Collaborate with experimentalists and theorists to validate extracted knowledge via in-situ spectroscopy, synchrotron work, and high-throughput synthesis—and present your results at leading AI and materials conferences Your Profile A completed university degree (Master’s or equivalent) with excellent grades in computer science, materials science, physics, or a related discipline Practical experience in data science, including the application of machine learning (ML) methods or large language models (LLMs) Proficiency in Python programming and confident use of Unix/Linux environments; ideally experience with version control systems (e.g., Git) Interest in or experience with semantic web technologies, including metadata schemas, ontologies, or knowledge graphs Excellent command of written and spoken English Strong communication and teamwork skills, and the ability to work effectively in an interdisciplinary and collaborative research environment 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 offer ideal conditions for you to complete your doctoral degree: A dynamic, interdisciplinary research environment at the forefront of materials informatics Comprehensive training courses and individual opportunities for personal and professional further development The opportunity to attend national and international conferences Optimal conditions for work-life balance, including a family-friendly corporate policy, flexible working hours, the option for home office days, and 30 vacation days per year A creative work environment at a leading research facility, located on an attractive research campus at the TZA Aachen and the Forschungszentrum Jülich Flexible working hours in a full-time position with the option of slightly reduced working hours Targeted services for international employees through our International Advisory Service Place of employment: Jülich/Aachen The position is for a fixed term of 4 years. Pay in line with 80% of pay group 13 of the Collective Agreement for the Public Service (TVöD-Bund) and additionally 60% of a monthly salary as special payment ("Christmas bonus"). Pay higher than the basic pay may be possible. Application We are looking forward to your application including: CV University degree certificates Grade transcripts Two references and/or letters of recommendation (e.g., by a previous supervisor) Motivation letter Please ensure that relevant experience is clearly demonstrated and briefly highlighted in your motivation letter. 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. The job will be advertised until the position has been successfully filled. You should therefore submit your application as soon as possible. Please note that for technical reasons we cannot accept applications via email. If you apply for this position please say you saw it on Physicaloxy

The IAS-9 focuses on data-driven methods tailored to challenges in the physical sciences and engineering. The research group "Knowledge Engineering for Materials Science" applies semantic technologies to improve data interoperability, reuse, and reasoning in materials research. Our work focuses on using ontologies and knowledge graphs to structure materials data and embed physical meaning into datasets, while leveraging techniques such as Large Language Models (LLMs) to improve semantic enrichment. We also contribute to the development of domain ontologies, metadata standards, and software tools that enable FAIR data practices across scientific workflows. Our work aligns with Helmholtz-wide and national data initiatives including NFDI-MatWerk, and contributes to shaping a sustainable, AI-ready research data infrastructure.

Your Job

Join an interdisciplinary team that brings state-of-the-art AI research together with cutting-edge materials science and physics. Depending on your background, you will work collaboratively on the following tasks with either a stronger model-development or application focus:

Design knowledge-graph-augmented transformers and retrieval-augmented generation (RAG) pipelines that enable semantic querying and reasoning over materials-science/physics corpora
Develop pipelines for semantic enrichment of unstructured data, including entity recognition, relation extraction, and automatic ontology alignment in physics and materials domains
Build and maintain ontologies, OWL/RDF knowledge graphs, SPARQL endpoints, and open benchmarking suites to guarantee FAIR, reusable research data
Mine and link structure-property relationships from DFT, MD, phase-field, TEM/SEM, and other multimodal datasets from simulation and experiment
Develop benchmarking protocols and toolkits to evaluate AI models on materials science tasks, and integrate semantic-AI services into high-throughput GPU/HPC workflows, contributing to data management, metadata structuring, and semantic annotation
Collaborate with experimentalists and theorists to validate extracted knowledge via in-situ spectroscopy, synchrotron work, and high-throughput synthesis—and present your results at leading AI and materials conferences

Your Profile

A completed university degree (Master’s or equivalent) with excellent grades in computer science, materials science, physics, or a related discipline
Practical experience in data science, including the application of machine learning (ML) methods or large language models (LLMs)
Proficiency in Python programming and confident use of Unix/Linux environments; ideally experience with version control systems (e.g., Git)
Interest in or experience with semantic web technologies, including metadata schemas, ontologies, or knowledge graphs
Excellent command of written and spoken English
Strong communication and teamwork skills, and the ability to work effectively in an interdisciplinary and collaborative research environment

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 offer ideal conditions for you to complete your doctoral degree:

A dynamic, interdisciplinary research environment at the forefront of materials informatics
Comprehensive training courses and individual opportunities for personal and professional further development
The opportunity to attend national and international conferences
Optimal conditions for work-life balance, including a family-friendly corporate policy, flexible working hours, the option for home office days, and 30 vacation days per year
A creative work environment at a leading research facility, located on an attractive research campus at the TZA Aachen and the Forschungszentrum Jülich
Flexible working hours in a full-time position with the option of slightly reduced working hours
Targeted services for international employees through our International Advisory Service

Place of employment: Jülich/Aachen

The position is for a fixed term of 4 years. Pay in line with 80% of pay group 13 of the Collective Agreement for the Public Service (TVöD-Bund) and additionally 60% of a monthly salary as special payment ("Christmas bonus"). Pay higher than the basic pay may be possible.

Application

We are looking forward to your application including:

CV
University degree certificates
Grade transcripts
Two references and/or letters of recommendation (e.g., by a previous supervisor)
Motivation letter

Please ensure that relevant experience is clearly demonstrated and briefly highlighted in your motivation letter.

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.

The job will be advertised until the position has been successfully filled. You should therefore submit your application as soon as possible.

Please note that for technical reasons we cannot accept applications via email.

If you apply for this position please say you saw it on Physicaloxy

Apply

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