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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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degree in AI, Computing Science, Mathematics, or Data Science. Strong coding, communication and organizational skills. Demonstrable experience with using machine learning packages (e.g., PyTorch
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cultural analysis; contributing to the development of a theory of the queer intellectual in close collaboration with the research team (including the PI and the Postdoc); producing a PhD dissertation, as
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machine learning packages (e.g.PyTorch). Completed academic courses in AI or machine learning. Interest in societal, ethical and philosophical questions. We consider it an advantage if you bring one or more
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organizational skills. Demonstrable experience with using machine learning packages (e.g., PyTorch). Completed academic courses in AI or machine learning. We consider it an advantage if you bring experience with
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. You will combine technical work on machine learning with qualitative analysis of how AI systems are interpreted and used in organisational decision-making. Join the Human-Centred Computing group
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communicate your findings through manuscripts and presentations, mentor BSc and MSc students, and contribute to teaching activities and academic outreach. Where to apply Website https://www.academictransfer.com
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conferences Publish results in international peer-reviewed journals Support and contribute to teaching activities within the department (max. 10%). Where to apply Website https://www.academictransfer.com/en
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, including abstract geospatial workflows; design AI- and machine-learning-based methods that automatically describe and model geodata sources using textual metadata (NLP) and the geodata itself; contribute
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Studies (CCSS), where you will interact with researchers working on a broad range of complex systems. Where to apply Website https://www.academictransfer.com/en/jobs/359824/phd-position-in-statistical-phy