Research Associate

Join the Cluster of Excellence "

BlueMat: Water-Driven Materials

" and contribute to one of Europe's most exciting research initiatives. Collaborate with a dynamic, interdisciplinary team that combines science, sustainability, and technology to create a better future. Help shape the next generation of sustainable materials inspired by nature's most powerful resource: water.

For the Institute of Machine Learning in Virtual Materials Design at Hamburg University of Technology

for 1 February 2026 we are looking for a

Research Associate (m/f/d) -

Wissenschaftlicher Mitarbeiterin

to conduct research within the CrossArea Data as part of the BlueMat Cluster of Excellence. The position is full time and fixed-term until 31 January 2029. The remuneration is in accordance with salary group 13

TV-L

(collective agreement for the public service of the federal states).

You will explore large language model (LLM) methods for understanding, organizing, and enriching complex scientific data in BlueMat. Your work will focus on using LLMs to interpret diverse experimental and simulation records, extract key entities and parameters, and generate high-quality metadata and provenance information. The goal is to create adaptive, FAIR-aligned data pipelines that help researchers find patterns, test hypotheses, and accelerate materials discovery across BlueMat's research areas.

YOUR CONTRIBUTIONS

• Design and refine LLM-based workflows for data interpretation, annotation, and curation across heterogeneous sources
• Develop modular tools for semantic parsing, metadata generation, and provenance tracking
• Investigate strategies for adapting foundation models to scientific domains (e.g., fine-tuning, prompt design, retrieval-augmented generation)
• Collaborate with BlueMat partners to integrate LLM insights with experimental, modeling, and imaging data
• Publish research results and contribute to open, reproducible software for data-driven materials science

YOUR PROFILE

Essential qualification

• Completed university degree (master's degree or equivalent), in the subject machine learning, computer science, data science, computational engineering, or related fields

Required knowledge and personal skills

• Solid background in large language models, natural language processing, or machine learning
• Proficiency in Python and experience with data handling or AI frameworks (e.g., PyTorch, Hugging Face, LangChain)
• Very good English required (at least B2/C1 level according to CEFR) – German is not mandatory
• Curiosity and teamwork skills for working in interdisciplinary teams

Desired knowledge and personal skills

• Experience with multimodal scientific data or ontology-based data management
• Interest in applying LLMs to scientific discovery, data infrastructure, or research automation
• Knowledge of materials science and engineering is advantageous

OUR OFFER

• Scientific qualification with the opportunity to pursue a PhD in a leading research cluster
• Access to BlueMat Academy: offering training workshops, German language support, mentoring, and career development
• Participation in international conferences, research stays (e.g. at our research partner institutions ETH Zurich and Columbia University), and collaborative networks
• Flexible working conditions, 30 vacation days, family-friendly policies
• Access to on-campus fitness facilities and health promotion programs

For further information please contactProf. Dr. Roland Aydin at we are pleased to support you.

Inclusive excellence drives better science. We actively seek female & international researchers from all walks of life, valuing non-linear careers and diverse perspectives across cultures, disciplines, and identities. We explicitly welcome applications from persons with severe disabilities and those with equivalent status

as defined in Section 2 of the German Social Code, Book IX (SGB IX).

The Hamburg University of Technology stands for equal opportunities

as well as appreciative and respectful cooperation.

Please submit your complete application documents (cover letter, CV, degree certificates) via the online application system, quoting the vacancy ID 30225WBMMEXK5, by 12 November 2025.