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                Field
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                or Nanophysics. * The candidate should also hold a Bachelor's degree in Physics or Materials Engineering. * The candidate should have recently completed the PhD: time after PhD defense should not exceed 1 year at 
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                NanoChemistry and Materials Group (NANOUP) is focused on controlling the supramolecular assembly of molecules, biomolecules and nanoscale building blocks at the nanometer scale for the design of novel functional 
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                computational chemistry techniques and data-driven approaches to optimize the properties of novel polymer-based materials. Key duties The successful candidate is expected to: Build and evaluate chemical databases 
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                also involve significant materials characterisation using techniques such as X-ray diffraction, and spectroscopic analysis using NMR and photoluminescence. The post holder will make use of the extensive 
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                Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living 
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                using techniques such as X-ray diffraction, and spectroscopic analysis using NMR and photoluminescence. The post holder will make use of the extensive materials and device fabrication and characterisation 
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                Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living 
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                build the sustainable companies and societies of the future. The division of Materials Science at LTU invites young researchers in the field of Engineering Materials, to apply for a position to strengthen 
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                To be considered for this role, you will ideally have: PhD in Computational Chemistry, Materials Science, or a related field. Strong background in computational chemistry techniques, including molecular 
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                will contribute to the development of a new simulation-based pre-training framework for building more robust and trustworthy machine learning-based clinical prediction models. Funded by the Medical