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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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valued in food system governance. This PhD will be based within the Soil Biology Lab, in collaboration with the Netherlands Institute of Ecology and the Farming Systems Ecology group. Your qualities As a
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youth in schools, including social media data donation. As a PhD candidate, your tasks will include: instrument selection; setting up the social media data donation in collaboration with experts
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model the remarkable learning efficiency of the human visual system. The project is an interdisciplinary collaboration between the the Machine Learning group at CWI in Amsterdam (Prof.dr Sander Bohte) and
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(LLMs) and state of the art methods in NLP. Has a demonstrable interest in misinformation countering or climate communication. Is willing to acquire knowledge of Social Network Analysis methods and
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., machine learning, stochastic dynamic programming, simulation). Affinity with (food) supply chain management is preferred. To collaborate with and to co-supervise MSc thesis students and internship students
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depth. You organise your work efficiently, take initiative, and are able to work independently when needed. You’re open to feedback and eager to learn from it, and you enjoy collaborating within
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team consisting of scientific staff and PhD candidates from different disciplines and will collaborate with a small team of software developers working on AI prototypes and the required supportive
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will collaborate with researchers from various disciplines at iHub, Radboud’s interdisciplinary research hub on digitalisation and society. Lastly, you will also collaborate with other researchers from