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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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. The SHIELT project is in collaboration with Wageningen Food Biobased Research (WFBR) and various industrial partners. Your duties and responsibilities include: use experimental and literature data to develop
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comparative and statistical approaches Exploring evolutionary hypotheses by clustering behavioural traits and mapping them onto phylogenetic trees Collaborating with a multidisciplinary team of biomechanists
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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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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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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