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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 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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, 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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colleague who: holds a Master’s degree in mathematics or computer science; has a solid foundation in category theory; is familiar with dependent type theory; is enthusiastic about learning advanced category
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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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, 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
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excellent academic record and demonstrate proven enthusiasm for academic research. You are familiar with qualitative research methods (ethnography, in-depth interviewing). Candidates willing to learn