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interdisciplinary environment within EMBRACER and international partners and apply advanced methods like Lagrangian tracking and reanalyses to reveal new insights into atmosphere-ice-ocean feedbacks in the Arctic
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have: experience or strong interest in geomechanical and/or hydrological modelling using continnum-based numerical methods (e.g., finite element method, finite difference method); experience or strong
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explanations from machine learning models. We will achieve this together by creating the first mathematical framework for explainable AI and developing new explanation methods. This will involve using tools from
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breeding and cultivation methods. Thus, we try to speed up the breeding of complex resilience traits for several crops in different growing systems. This collaborative effort involves four universities and
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), sustainable, and climate-adaptive crops. By combining plant biology, simulation modelling, and artificial intelligence we aim to develop smart breeding and cultivation methods. Thus, we try to speed up
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address these issues by developing mathematical foundations for XAI, and proving performance guarantees for new explanation methods using the same standards as in other parts of machine learning theory. As
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materials. Another major challenge is enabling motors to function in aqueous media, which opens up numerous opportunities for integrating motors with biological systems, i.e. to design responsive biohybrid
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or strong interest in geomechanical and/or hydrological modelling using continnum-based numerical methods (e.g., finite element method, finite difference method); strong quantitative and programming skills
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of the Dutch NWA consortium “PRELIFE”, https://www.originscenter.nl/prelife/ The origin of life remains one of science’s most profound and enduring mysteries. Despite numerous theories, no single explanation has
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set of equations, resulting in so-called moment models. The whole project covers model derivation, model reduction, and numerical simulations. In collaboration with the rest of the PI's group (4 PhD's