AI Engineer (m/f/d) for Method Development and Digitization for Collection-based Research

Senckenberg – Leibniz Institution for Biodiversity and Earth System Research (SGN), headquartered in Frankfurt am Main, is seeking to fill the following position in the Research Group Digital Collectomics at the Senckenberg Institute for Plant Form and Function starting as soon as possible

AI Engineer (m/f/d) for Method Development and

Digitization for Collection-based Research

Location:  Jena (Thuringia, Germany)

Employment scope:  Full-time (40 hours per week) / part-time options are available

Type of contract: The contract shall start at the earliest possible date and is limited to 2 years

Remuneration: Collective agreement of the German Länder, TV-L E 11

The Senckenberg Society for Nature Research is a member of the Leibniz Association and has been investigating the “Earth System” worldwide for more than 200 years, examining the past, analysing the present, and developing projections for the future.

We conduct integrative geobiodiversity research with the aim of understanding nature in all its complexity and diversity in order to preserve it as the foundation of life for future generations and to ensure its sustainable use. Across eight institutes and five research stations throughout Germany, scientists from more than 40 countries conduct research at the highest international level.

The Senckenberg Institute for Plant Form and Function (SIP) at Friedrich Schiller University Jena (FSU) is the eighth and most recently founded Senckenberg institute. Located in the vibrant university city of Jena in Thuringia and forming part of an international network, it brings together the renowned Herbarium Haussknecht with cutting-edge research on biodiversity change in the Anthropocene. 

The Junior Research Group Digital Collectomics deals with the development of automated methods revolving around the extraction of information from collections, with a strong focus on herbaria, as well as the digitization process and public display thereof.

We leverage the latest developments in computer vision and AI research to create broadly applicable methods that can deal with small to no available training data. Therefore, our research focuses primarily on foundation models, few- and zero-shot methods and domain generalization. In interdisciplinary cooperation with the local Herbarium Haussknecht and partners all over Senckenberg, we create cutting-edge approaches for publication in high-ranked computer vision, machine learning and interdisciplinary venues with the ambition to convert the novel methods into directly applicable tools usable in collection-based research at the Senckenberg and beyond.

The selected candidate will employ, adapt and assess methods from the literature in the areas of foundation models, few- and zero-shot methods and domain generalization in established team projects and novel projects, integrate the developed methods into the Senckenberg infrastructure and make developed methods usable for end users.