PhD Positions in Theoretical Ecology: Join the Theoretical Ecology Group at the University of Regensburg and contribute to cutting-edge research in ecological interactions, machine learning, and biodiversity. Various PhD positions are available, catering to different research interests and qualifications.
Designation
- PhD Position on Plant-Pollinator Interactions (3 years, 65% E13)
- PhD Position on Theoretical Ecology and Related Subjects (1+3 years, 70% E13)
- PhD Position on Ecological Machine Learning (bAImo project) (1+3 years, 65% E13)
- PhD Position on Ecological Machine Learning (BaySenseAI project) (1+2.5 years, 75% E13)
- Scientific Programmer / Data Scientist (100% E11, 2 years or could be made permanent)
Research Area
- Plant-Pollinator Interactions
- Theoretical Ecology, Macroecology, Ecological Statistics, Ecological Modelling
- Ecological Machine Learning related to insect abundance and biodiversity data integration
Location
University of Regensburg, Germany
Eligibility/Qualification
- PhD Positions: Applicants should have a strong academic background relevant to the specific research area. Experience in relevant methodologies (e.g., fieldwork, genetics, statistics) is beneficial.
- Scientific Programmer/Data Scientist: Relevant experience in statistical data analysis, curation of ecological data, and scientific programming (particularly with R).
Job Description
Duties include:
- Conducting independent research in the designated area.
- Collaboration with faculty members and contributing to group projects.
- Engaging in fieldwork and data collection/analysis as required by the project.
- For programming positions, maintaining and developing software tools to support research initiatives.
How to Apply
Interested candidates should apply via the university’s recruitment system. Specific links to the application form can be found in the job announcements provided in both German and English.
Last Date to Apply
- PhD Positions:
- Plant-Pollinator Interactions: Nov. 30, 2025
- Theoretical Ecology: Dec. 07, 2025
- Ecological Machine Learning (bAImo): Dec. 07, 2025
- Ecological Machine Learning (BaySenseAI): Dec. 07, 2025
- Scientific Programmer/Data Scientist: Dec. 07, 2025
Take this opportunity to advance your academic career and contribute to vital research at the intersection of ecology and technology.
