Job announcement ref. #08-25012

 
For the Senckenberg Museum of Natural History in Görlitz, the Senckenberg Gesellschaft für Naturforschung headquartered in Frankfurt (Main) is seeking the to fill the following position

 Postdoctoral researcher (m/f/d) in Environmental Data Science and Machine Learning for the project BoTiKI
 
 
Location:                                Görlitz

Employment scope:              full-time (40 weekly working hours) / part-time options are available

Type of contract:                  fixed-term contract until the end of the project:

30 November 2027

Remuneration:                      collective agreement of the German Länder, TV-L E 13
 
 
Founded in 1817, the Senckenberg Gesellschaft für Naturforschung (SGN) is one of the world’s major research institutions in the field of biodiversity. At our twelve sites in Germany, scientists from over 40 nations conduct cutting-edge research at an international level. At the Görlitz site, the renowned Senckenberg Museum of Natural History is located in a historic town within a region known for its unspoilt natural beauty.

Are you interested in applying your machine learning and deep-learning expertise to develop cutting-edge ecological and environmental research? The Senckenberg Gesellschaft für Naturforschung invites you to become part of an exciting project at the Senckenberg Museum of Natural History Görlitz (Saxony, Germany). We are looking for a motivated environmental data modeler – data scientist (m/f/d) to support the project BoTiKI (funded through the BMUKN ANK, ‘KI-Leuchttürme für Umwelt, Klima, Natur und Ressourcen’), to start as soon as possible.

Soil is a large reservoir of greenhouse gases (GHG). It can sequestrate or release potent GHG (CO2, CH4 and N2O). Despite the fact that soil fauna is crucial to GHG fluxes, the specific impact of soil fauna on emissions has not been researched in depth and constitutes a missing factor in soil GHG flux models.

BoTiKI aims at filling this knowledge gap and establish improved GHG models accounting for soil fauna. To achieve this, we create a rich AI-training dataset for multimodal inferences, combining computer-vision, environmental parameter measures and DNA data.

Your role will be central in data acquisition and foremost machine-learning models creation. You will collaborate closely with a dedicated team of soil fauna experts, ecological data modelers, computer-vision system engineers.

Your Tasks

• Establish data science pipelines, data-modelling strategies, model training, evaluation and application
• Lead or co-lead peer-review publications stemming from the project achievements
• Cooperate with project partners from the Zittau/Görlitz University of Applied Sciences in developing computer-vision systems for faunistic data analysis
• Present project goals and achievements in national and international scientific conferences
• Supervise technicians and students in training data annotation
• Implement analytic workflow in a functional, user-oriented application (CLI) to deliver the workflow and models to the community
• Cooperate with third-party IT provider to implement user-oriented functionalities (GUI and web-service)
• Participate in field work organization, sampling plan establishment and in-situ data acquisition
   
   

Your Profile

•   PhD in environmental sciences or computer science, with a proven track record in data modelling, machine learning and deep learning
• Previous research achievements supported by peer-reviewed publications
• Excellent knowledge of statistical/machine-learning and deep-learning algorithms
• Versatile data-science knowledge, including image and DNA sequences processing
• Programming skills in Python or other modern programming languages supporting AI and bioinformatics development
• Very good command of English (written and spoken)
• Ability to work in an interdisciplinary team, independent and structured working style
   
   
  Desirable Skills:
• Knowledge in web application and service development (e.g. user interfaces and APIs)
• Background in soil, ecological or environmental science
• Comfortable with/Accustomed to handling small invertebrates (mites, springtails)
• Good command of German (written and spoken)
• Class B driving license valid for Germany
   
   

We offer

• access to an international network of scientists, policymakers and research organizations
• an attractive job within the inspired and dynamic working environment of an internationally recognized research institution
• Flexible working hours – mobile working options – assistance with child care and care for family members („audit berufundfamilie“) – employee ID card with free admission to the Senckenberg museums – 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. Women are particularly encouraged to apply as they are underrepresented in the field of this position; in the case of equal qualifications and suitability, they will be given preference. 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.
   
   

How to apply?

 Please submit your application as a single PDF document, including:

• Cover letter (1 page) detailing your experience, skills, motivation and fit for this role
• CV
• Copies of your academic certificates
• List of publications

Reference #08-25012 should be mentioned in your application.

Application deadline: July 1st, 2025

**Please send your application to: recruiting@senckenberg.de

**Or apply online: https://www.senckenberg.de/en/career/apply-online/

 
Senckenberg Gesellschaft für Naturforschung     

Senckenberganlage 25

60325 Frankfurt am Main

E-Mail: recruiting@senckenberg.de
 
For specific questions about this role, please contact Dr. Clement Schneider at clement.schneider@senckenberg.de.
 
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 https://www.senckenberg.de/en/imprint/.
 
For further information about the Senckenberg Gesellschaft für Naturforschung please visit www.senckenberg.de.