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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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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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environments; experience coordinating complex workflows (e.g., between wet lab, proteomics and dry lab) and maintaining structured project documentation. The lab places strong emphasis on teamwork, where
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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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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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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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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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learning or neural networks), etc. The experience could come from lithium-ion, lithium-ion capacitor, sodium-ion, or other battery technologies. You have solid skills in Matlab/Simulink simulation tool
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Laboratory, which investigates how post-translational modification (PTM) networks regulate protein function, genome stability, and cellular decision-making. The laboratory builds on internationally recognized