PhD position in Machine Learning and Ecology Full-time NEW

Night pollinators – an overlooked component of pollination networks

The Faculty of Science at the University of South Bohemia is launching a transformative 4-year research project dedicated to nocturnal pollinators. Despite their fundamental ecological significance, nocturnal pollinators remain disproportionately understudied, leaving a critical knowledge gap in our understanding of complex pollination networks.

This project aims to bridge this global knowledge gap by developing and implementing state-of-the-art AI-powered models for the automated taxonomic identification of insects captured via high-resolution camera traps. You will build upon the prestigious SEPPI Biodiversa (Standardized European monitoring of plant-pollinator interactions; https://seppi-pollinate.weebly.com) initiative, extending its proven hardware and software frameworks into the challenging and complex nocturnal environment.

Key research Objectives:

• AI Innovation (Taxonomic Identification): Developing and optimizing deep-learning architectures (e.g., YOLO) for the automated detection and classification of nocturnal insect species.
• Ecological Network Analysis: Mapping the vital contribution of nocturnal species to pollination networks across both natural and restored meadows in the White Carpathian Mountains (a European biodiversity hotspot – for more information look at https://ser-insr.org/news/2017/12/29/grassland-restoration-in-the-white-carpathian-mountains).
• Effectiveness Assessment: Quantifying real-world impact through manipulative field experiments to measure pollen transfer and plant reproductive success.
• Botanical Synergy & Conservation: Identifying key Czech flora that support the full life cycles of nocturnal pollinators to establish a comprehensive conservation database.

Candidate Responsibilities

• design and optimization of AI detection and classification workflows for large-scale image data
• active participation in field research, including data collection and experimental monitoring of plant-pollinator interactions
• publication of at least three high-impact scientific papers as the core of the doctoral thesis
• present and disseminate research results at international events and seminars
• be an active member of the working group

What we offer

• duration: 4 years (standard PhD programme duration)
• anticipated start: April/May 2026 and will last for four years, which is a standard period of a doctoral programme at FSci USB (https://www.prf.jcu.cz/en/study-at-the-faculty/information-for-phd-students).
• salary: salary on the basis of local standards, including health insurance and social advantages. Monthly net income of 30,000 CZK (combined salary and state scholarship), totaling approx. 360,000 CZK (~14,730 EUR) annually
• location: České Budějovice, a vibrant university city with a strong community of international scientists and low living costs by European standards

Required skills of PhD candidate

• MSc degree in a relevant field (Informatics, Computer Science, Ecology, or Biology).
• strong Python programming skills
• proven experience with Machine Learning and image processing (Deep Learning/Object Detection)
• strong analytical and problem-solving skills, with the ability to work both independently and in an interdisciplinary team
• fluency in English, both written and spoken with excellent communication skills
• technical and field assets: experience with camera traps/sensor networks, interest in biodiversity, and a valid EU driving license

How to apply

The call for applications remains open until the position is filled.

Priority will be given to applications received by April 15, 2026. Following a pre-selection process, shortlisted candidates will be invited for an online interview.

Requirements for Application: Please submit the following documents in a single PDF file to the principal investigator, Assoc. Prof. Jana Jersakova ([Please click the Apply button for the link or address]):

1. Cover Letter specifying your motivation and relevant technical expertise.
2. Curriculum Vitae including a list of publications (maximum 3 pages).

Tagged as: Computer Science, Earth Science, Environmental Science