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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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). Completed academic courses in AI or machine learning. We consider it an advantage if you bring experience with Reinforcement Learning, Deep Learning and/or Explainable AI, demonstrated for example through
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Reinforcement Learning, Deep Learning and/or Explainable AI, demonstrated for example through coursework or research projects. Our offer a position for 18 months, with an extension to a total of four years upon
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of the following: Experience with Explainable AI. Experience with Deep Learning. An interdisciplinary background / interdisciplinary training. Have followed courses in Psychology or Philosophy
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microscopy techniques. Working closely with a postdoctoral researcher to use your experimental observations as constraints for micromagnetic simulations, systematically assessing how size, shape, and
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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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bundles, and related subjects. Your job Enumerative geometry is a field of algebraic geometry with a history dating back to Ancient Greece and with exciting modern developments due to deep connections with
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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