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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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and democratic participation of citizens. You will focus on developing adaptive learning systems that enhance the transparency and contestability of AI decisions through personalized, multimodal
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participation of citizens. You will focus on developing adaptive learning systems that enhance the transparency and contestability of AI decisions through personalized, multimodal explanations. Your job AI is
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this. Implement and test such a framework, in a clinical setting. Help teach explainable AI to bachelor’s students and master's students and/or decision makers. For example, by developing workshops and training
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and understanding of complex biological systems and biodiversity. You will get the opportunity to learn about both simple and complex biological models, computer programming, data visualisation, and
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sizes and frequencies by: Measuring rock fractures from UAV data using manual and automated mapping approaches (e.g., machine learning, convolutional neural networks). Monitoring physical weathering
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Social Science, or related field; has strong affinity with the study of families and economic inequality; has experience with both quantitative and qualitative research (or is motivated to learn both types
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to innovation and transition in action. Next to working in the context of the larger Ombion-CPBT project, as a PhD researcher you will have the opportunity to acquire and develop reflexive, participatory and
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to acquire and develop reflexive, participatory and transdisciplinary research skills. You will be part of a vibrant and supportive interdisciplinary, international community at the Copernicus Institute
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, experimental designs and applying (multivariate) statistical techniques; excellent written and verbal communication skills in English; independence, eagerness to learn and the ability to work in an