Bio
Thijs is a research associate developing new machine learning methods for AI-driven biodiversity monitoring. Before joining the Turing, Thijs trained as a physicist at the Radboud University, the Netherlands (MSc, 2019), before completing his DPhil in computational neuroscience at the University of Oxford (2023). His neuroscience focused on inferring structure from high-dimensional brain activity and relating this to cognitive functions such as perception. In the final year of his PhD, Thijs joined the Turing internship network working with the Peak District to create a high-resolution land cover map of this National Park. Currently as research associate, he continues to focus on high-dimensional data analysis, merging remote sensing imagery and citizen-science wildlife observations to predict biodiversity in the UK. To this end he collaborates with UK Centre for Ecology & Hydrology and is part of the Turing’s Environment & Sustainability Grand Challenge.
Research interests
Thijs’s research revolves around using advanced machine learning techniques to address scientific questions in biology. Currently working on biodiversity monitoring, he primarily analyses remote sensing data (satellite images, aerial photography), citizen-science wildlife observations and other geospatial data sets. As such, he is particularly interested in methods that can infer efficient feature embeddings from high-dimensional data, predictive models, and techniques that minimise data requirements such as self-supervised and unsupervised learning. Further, Thijs is an advocate for reproducible data visualisation, open-access science and open-source software.