Master Thesis

vor 4 Wochen

Leipzig, Deutschland Helmholtz Centre for Environmental Research Vollzeit

The UFZ

The Helmholtz Centre for Environmental Research (UFZ) with its 1,100 employees has gained an excellent reputation as an international competence centre for environmental sciences. We are part of the largest scientific organisation in Germany, the Helmholtz association. Our mission: Our research seeks to find a balance between social development and the long-term protection of our natural resources.

The job

Plant-pollinator interactions are crucial for the pollination of most plants and play a vital role in maintaining biodiversity. While most research focuses on diurnal pollinators like bees and butterflies, moths (Lepidoptera) are key nocturnal pollinators whose importance may be underappreciated. Emerging evidence shows that moths pollinate a wide range of plant species across diverse ecosystems. However, limited research exists on nocturnal plant-pollinator interactions due to the challenges of capturing these interactions in darkness without disrupting natural behavior.
Traditional methods to monitor night pollinators involve light traps (which disrupt behavior) or watching flowers using night vision goggles. The rise of AI-driven biodiversity monitoring offers promising new methods to monitor night pollination. Automated camera traps capable of AI-based insect detection to trigger image capture are currently in development by our team for diurnal pollinator monitoring. Adapting these tools for monitoring nocturnal pollination is the next exciting frontier.
In this project, we aim to develop and test an automated camera trap with night vision capability, specifically designed to monitor plant-pollinator interactions in the field, with a focus on nocturnal pollinators. This advancement would significantly expand our understanding of pollinator networks, complementing daytime data with critical nocturnal insights.
The master thesis is part of the (Standardized European monitoring of Plant-Pollinator Interactions), which aims to develop and test protocols for the automated monitoring of pollinators and their interactions with plants, with the goal of scaling this approach across Europe. This research offers the opportunity to advance the understanding of full plant-pollinator networks, integrating both diurnal and nocturnal interactions.

The position to prepare the Master's thesis is limited to 6 months and will be supervised in Leipzig at the iDiv centre (German Centre for Integrated Biodiversity Research). The Master’s student will have the opportunity to work with a supportive team of mentors with expertise in AI, hardware development and pollination ecology.

Your tasks

In this Master’s thesis, you will modify an existing to enable nocturnal monitoring of plant-pollinator interactions. You will test these DIY camera traps in a field study within Leipzig city. The tasks include:

Development of software and hardware components for an Raspberry Pi-based automated camera trap that can capture nocturnal plant-pollinator interactions Testing the DIY camera traps in a dark lab and at night in field settings, including staying in the field during late hours to evaluate the equipment Field data collection of plant-pollinator interactions using the developed camera traps, as well as traditional methods (e.g. Flower-Insect Timed Count) Optional: Microscopy work to identify captured nocturnal pollinators Data processing, annotation, and AI classification of captured images based on already established workflows Data analysis

We offer

Excellent supervision that supports your personal and professional development Exciting insights into the work of a leading research institute The chance to work in interdisciplinary, international teams and benefit from a wide range of perspectives The opportunity to contribute and actively shape your own ideas and impulses
right from the start Modern technical equipment and IT service to optimally support your work

Your profile

Background in Computer Science, Bioinformatics, Biology, Ecology, or a related field with a passion for and experience working with microcomputers such as Raspberry Pis Basic programming skills in Python Experience or interest in pollination ecology and insect classification is a plus Experience with R is a plus Fluency in spoken and written English Willingness to conduct fieldwork during nighttime in a secure location, including staying up late

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