Research Associate (m/f/d) - Wissenschaftliche*r Mitarbeiter* at the Institute of Machine Learning in Virtual Materials Design

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 Technologyfor 1 February 2026 we are looking for aResearch Associate (m/f/d) -Wissenschaftlicher Mitarbeiterinto 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 13TV-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 CONTRIBUTIONSDesign and refine LLM-based workflows for data interpretation, annotation, and curation across heterogeneous sourcesDevelop modular tools for semantic parsing, metadata generation, and provenance trackingInvestigate 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 dataPublish research results and contribute to open, reproducible software for data-driven materials scienceYOUR PROFILEEssential qualificationCompleted university degree (master's degree or equivalent), in the subject machine learning, computer science, data science, computational engineering, or related fieldsRequired Knowledge And Personal SkillsSolid background in large language models, natural language processing, or machine learningProficiency 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 mandatoryCuriosity and teamwork skills for working in interdisciplinary teamsDesired knowledge and personal skillsExperience with multimodal scientific data or ontology-based data managementInterest in applying LLMs to scientific discovery, data infrastructure, or research automationKnowledge of materials science and engineering is advantageousOUR OFFERScientific qualification with the opportunity to pursue a PhD in a leading research clusterAccess to BlueMat Academy: offering training workshops, German language support, mentoring, and career developmentParticipation in international conferences, research stays (e.g. at our research partner institutions ETH Zurich and Columbia University), and collaborative networksFlexible working conditions, 30 vacation days, family-friendly policiesAccess to on-campus fitness facilities and health promotion programsFor further information please contactProf. Dr. Roland Aydin at roland.aydin@tuhh.de- 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 statusas defined in Section 2 of the German Social Code, Book IX (SGB IX).The Hamburg University of Technology stands for equal opportunitiesas 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.