Postdoctoral researcher in Digitalized and Data-Driven Materials Discovery (F / M / D)

Postdoctoral researcher in Digitalized and Data-Driven Materials Discovery (F / M / D) at INM - Leibniz-Institut für Neue Materialien gGmbH | softgarden

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INM is an internationally leading centre for interdisciplinary research on (bio)hybrid materials, and their exploitation in materials-based solutions for a sustainable world with increasing digitalization and medical needs. Our research integrates materials chemistry, biological processes, physical analysis, and process engineering. We aim for leadership in the fields of opto-interactive, electro-integrative and bio-intelligent material systems. In these areas, we actively seize opportunities to transfer our scientific results into materials-driven innovation.

Postdoctoral researcher in Digitalized and Data-Driven Materials Discovery (F / M / D)

Full Time

Hybrid

Campus, 66123 Saarbrücken, Deutschland

With Professional Experience

1 / 16 / 26

A focus of our research are Engineered Living Materials (ELMs), an emergent class of materials at the interface between materials science and synthetic biology, with huge potential for sustainable innovation in different technological fields. The multidisciplinary component of our research requires intensive information and data exchange between multiple disciplines. We work on semantically structured data and interfaces, shared workflows, and contribute to engaging the growing ELM community in digitalized research.

To coordinate and support the digitalization of the ELM community, we are seeking a

Postdoctoral Researcher with expertise in Digitalization and Data Science (f,m,d)

to work within the collaborative network MaterialVital Hub, recently funded by the Federal Ministry of Research, Technology and Space (BMFTR) in collaboration with the MaterialDigital Platform. The candidate will be part of a team working together with experimental scientists and research data management experts at INM-Leibniz Institute for New Materials, with experts in ontologies and data platforms at IFW-Fraunhofer Institute for Mechanics of Materials and with bioinformaticians at the University of Freiburg.

Your roles

• Contribute to the digitalization of the materials / data development workflows of selected ELM use cases by developing code to create semantically annotated data structures.
• Development of metadata schemes for typical methods in ELM research which are transferable to electronic Lab Notebooks and data platforms of relevance for other researchers within the MaterialVital Hub.
• Lead the development of ELM-dedicated ontology for interoperability within the MaterialVital Hub in cooperation with researchers and MaterialDigital partners.
• Demonstrate application cases of Data Science to ELM research based on structured data from the MaterialVital Hub.
• Support and promote digitalization of the ELM community within the MaterialVital Hub in close interaction with MaterialDigital Platform and NFDI.
• Attend workshops and disseminate your work within the ELM community by presenting results at conferences and scientific events.
• Document your work and report to project coordinators and stakeholders.

Your profile

We offer

The position is initially for 3 years.

Interested?

We look forward to your application!

Please upload your CV and motivation letter, by February 15th 2026 via the online application system.

For further information on this position, please contact INM Scientific Director Prof. Dr. Aránzazu del Campo (aranzazu.delcampo@leibniz-inm.de).

The INM practices an open and appreciative corporate culture in which the existing diversity is promoted and lived. The institute is an equal opportunity employer with a certified family-friendly policy, and it provides offers for a better work-life balance, flextime, and mobile working. We promote professional opportunities for women and strongly encourage them to apply. Full time jobs can be generally divided. Severely disabled applicants with equal qualifications and aptitude will be given preferential consideration.

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