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PhD position: Global soil mapping with process-informed machine learning Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 36 to 40 Application deadline
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background in hydrology, earth system science, atmospheric science, agroecology or other appropriate fields. You will work on the project “Physics-informed AI-modelling of land surface processes in a global
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3 Apr 2026 Job Information Organisation/Company Utrecht University Research Field Computer science » Cybernetics Computer science » Programming Engineering » Computer engineering Engineering
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Switzerland, the project aims to better understand urban densification as a contested process of land rent creation and redistribution, thereby exposing tensions among actors, values, discourses, and
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physics, stochastic processes, statistical inference, and epidemiology, with SARS-CoV-2 and influenza as key case studies. Your job In this project, you will develop a quantitative theory of evolution
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approached as a situated process emerging through the interaction of data, models, professional judgement, and organisational context. Depending on your interests and empirical setting, the project will
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decision-making and reinforcement learning Enhancing transparency and contestability of decision-making processes, taking a multimodal approach to reveal the reasoning behind complex AI-driven planning and
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)”, funded by the Swiss National Science Foundation. Focusing on case studies in the Netherlands and Switzerland, the project aims to better understand urban densification as a contested process of land rent
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predictive theory for rapidly evolving viruses. You will work at the interface of theoretical physics, stochastic processes, statistical inference, and epidemiology, with SARS-CoV-2 and influenza as key case
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the magnetization will propagate without scattering and loss of energy. Such structures could potentially be a novel building block for future computer chips and more sustainable IT technologies. Your research will