Postdoc (mfd) for AI-driven Method Development 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

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. The position is limited to 2 years

Remuneration: Collective agreement of the German Länder TV-L E13

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 interdisciplinary cooperation with the local Herbarium Haussknecht and other 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 Senckenberg and beyond.

The selected candidate will research novel methods in the areas of foundation models few- and zero-shot methods and domain generalization in established team projects and novel projects in order to advance collection-based research.

• Work in (team) projects by contributing co-developing executing implementing and evaluating ideas for novel AI-driven image and data analysis approaches for collection data
• Establishment of own projects research questions and approaches for image and data analysis of collection data with focus on broadly applicable methods able to deal with little available data (e.g. zero-shot few-shot or weakly supervised approaches and foundation models)
• Publication and presentation of research results in high-ranked machine learning computer vision and interdisciplinary venues
• Curation and archiving of datasets
• Keeping up with current developments in computer vision and machine learning research
• Support of funding acquisition and establishment of the new group at Senckenberg including participation in outreach events
• Advising of colleagues and students including supervision of bachelors and masters theses

• A doctoral degree (or in the stage of its finalization) in Computer Science or a related subject with a focus on computer vision and/or deep learning ideally with interdisciplinary experience or a doctoral degree (or in the stage of its finalization) in Biology with strong computer vision and deep learning background (application or research)
• Experience with

Current methods tools languages and frameworks in computer vision and deep learning (e.g. Linux Bash Python Pytorch Gradio Numpy Pandas Huggingface Ollama)

LLMs and foundation models in general

• Good organizational abilities reliability and efficiency
• An open mind-set curiosity and a strong motivation to excel in science
• A collaborative attitude and good communication skills
• Excellent written and oral communication and presentation skills in English

• German language skills
• Experience with computing on clusters
• Experience with funding-acquisition

• An attractive responsible and challenging position in a globally recognized research institution with motivated and professional colleagues
• A vibrant international team of scientists in Jena and all of Senckenberg with collection-based research programs focusing on understanding the Anthropocene Biodiversity Change
• Excellent infrastructure and collaboration opportunities at SIP embedded in a lively research environment in Senckenberg and the Friedrich Schiller University Jena
• Opportunities to participate in teaching and outreach activities e.g. at Friedrich Schiller University Jena
• flexible working hours mobile working options assistance with child care and care for family members (audit berufundfamilie) annual special payment collectively agreed vacation entitlement company pension plan

Senckenberg is committed to diversity. We benefit from the different expertise perspectives and personalities of our staff and welcome every application from qualified candidates irrespective of age gender ethnic or cultural origin religion and ideology sexual orientation and identity or disability. Applicants with a severe disability will be given special consideration in case of equal suitability. Senckenberg actively supports the compatibility of work and family and places great emphasis on an equal and inclusive work culture.

We look forward to receiving your application. Please upload your comprehensive documentation consolidated into a single PDF file via our online application portal by 02 August 2026:

• A letter of motivation
• A CV
• Publication list and academic achievements
• Copies of certificates and diplomas

Apply now via: note that interviews are planned to take place from August 25th onwards. We will provide further details in due course.

Join us in advancing ecological research through innovative technology! We look forward to your application!

Senckenberg Gesellschaft für Naturforschung

Senckenberganlage 25

60325 Frankfurt am Main

For specific questions about this role please contact Dr. Matthias Körschens at .

For specific questions regarding the application process please contact .

For data protection information on the processing of personal data as part of the application and selection process please refer to the privacy policy on our homepage at further information about the Senckenberg Gesellschaft für Naturforschung please visit