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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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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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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
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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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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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construction. Position 2: CFD-driven digital modeling and thermal prediction The objective of this position is to refine the cooling system design and to develop a digital thermal model capable of fast and
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bioinformatic workflows, including diagnosing computational and analytical problems when established pipelines or software are not directly applicable. Experience working with complex, heterogeneous
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published in international peer reviewed journals. Understanding of the atmosphere and its modelling is essential. • Model development of WRF-Chem is complex. Documented experience using FORTRAN, C
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of battery test data and you are comfortable working systematically with complex technical problems. Experience with industrial collaboration and battery management systems will be an advantage, as
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analysis and you are comfortable working systematically with complex technical problems. Experience with industrial collaborations and working with EU granted projects will be an advantage, as will a