PhD in Biostatistics: Biodiversity Monitoring with Citizen-Science Data

This PhD position at KU Leuven focuses on advancing statistical methods for biodiversity monitoring using citizen-science data. The successful candidate will design fast, scalable occupancy and abundance mapping tools in R/Stan, leveraging GPU acceleration for efficiency. The research will involve validating these models on real-world datasets and ensuring practical applicability for conservation efforts. The project bridges biostatistics, computational statistics, and ecological data science, offering a unique opportunity to contribute to both methodological innovation and environmental monitoring. Applicants should have a strong quantitative background and enthusiasm for interdisciplinary research.

• Develop Bayesian statistical models for analyzing large-scale biodiversity datasets collected via citizen-science platforms.
• Implement fast and scalable occupancy and abundance mapping tools using R/Stan, with a focus on GPU optimization for performance.
• Validate the developed models using real-world ecological datasets to ensure accuracy and applicability.
• Collaborate with ecologists and conservation scientists to translate statistical methods into practical tools for biodiversity monitoring.
• Publish research findings in high-impact journals and present results at international conferences.
• Contribute to the development of open-source statistical tools and methodologies for the broader research community.
• Participate in interdisciplinary workshops and training sessions to enhance skills in ecological data science.