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recognition events to colonization strategies. Within this framework, the advertised postdoctoral project is specifically focused on complex data analyses of plants for improved symbiotic associations with soil
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for fluid mechanics. The position is for three years, is based in Copenhagen, at AAU's waterfront campus in Sydhavn, and starts 1st of October or soon thereafter. In this position, you will take a leading
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addressing complex fracture problems, and you are comfortable translating detailed micro and mesoscale mechanisms into models that can be used for engineering design and assessment. You have a track record of
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for compositionally complex, defect-containing and structurally disordered crystalline materials. Develop, train, validate and benchmark machine-learned interatomic force fields for multicomponent inorganic energy
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of two years. About the Project The successful candidate will advance the algorithmic and theoretical foundations of reinforcement learning applied to complex, high-dimensional dynamical systems
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chemistry and the synthesis of organometallic complexes. The candidate must have experiences with the technologies for carbon capture and utilisation. The candidate must have good communication skills and
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other environmental stresses. However, identifying the genes underlying these complex traits remains challenging. This project will combine plant genomics, artificial intelligence, and evolutionary
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of the position is the development and application of finite element methods for modelling static strength and fatigue behaviour, as well as design and optimization of complex structures. You will work
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thereafter. Explaining complex AI models is a key challenge for ethically responsible AI. Explainable AI (XAI) research aims to provide relevant information to assist developers and users in analyzing AI
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organometallic chemistry and the synthesis of organometallic complexes. The candidate must be with the technologies for plastic recycling. The candidate must have good communication skills and experience in