[PhD Opening at Utrecht University] Hybrid Machine Learning for Global Soil Mapping
"Chatzimparmpas, A. \(Angelos\) via dmanet" <[email protected]>
| Newsgroups | gmane.science.mathematics.discrete |
|---|---|
| Message-ID | <[email protected]> |
Dear colleagues, We are hiring a PhD candidate at Utrecht University! Are you interested in developing new machine learning methods for a challenging real-world application? This PhD project focuses on hybrid / process-informed machine learning for global soil mapping. The goal is to develop next-generation methods for geospatial prediction by integrating scientific process knowledge into modern AI approaches. The research will explore methods such as neural networks, tabular transformers, and Bayesian approaches, with applications to large-scale spatiotemporal and environmental data. We are particularly interested in candidates with a strong quantitative background, for example in data science, machine learning, statistics, applied mathematics, computational geosciences, or related fields, who enjoy working on interdisciplinary problems and have programming experience in Python, JS, and/or R. Interested? More information and application details can be found here: https://www.uu.nl/en/organisation/working-at-utrecht-university/jobs/phd-position-develop-hybrid-machine-learning-for-global-soil-mapping Application deadline: 6 September 2026. For more information, please contact Madlene Nussbaum at [email protected] . Please feel free to share this opportunity within your network. With best regards, Angelos Chatzimparmpas On behalf of Madlene Nussbaum ********************************************************** * * Contributions to be spread via DMANET are submitted to * * [email protected] * * Replies to a message carried on DMANET should NOT be * addressed to DMANET but to the original sender. The * original sender, however, is invited to prepare an * update of the replies received and to communicate it * via DMANET. * * DISCRETE MATHEMATICS AND ALGORITHMS NETWORK (DMANET) * http://www.zaik.uni-koeln.de/AFS/publications/dmanet/ * **********************************************************