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Field
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characterisation techniques such as in situ scanning tunnelling microscopy. This combination will be used to investigate the structure-property relations of well-defined electrocatalyst materials as
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the Ministerial Order on the Appointment of Academic Staff at Universities (the Appointment Order) and the Ministerial Order on Job Structure for Academic Staff at Universities (in Danish) and protocol on certain
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 17 hours ago
construction of and analysis of a large dataset that integrates data from multiple sources. The role includes: ● Leading dataset construction efforts. ● Supporting the new Center for Migration and Diaspora
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practices for sourcing and securing critical supplies and materials across partner organisations (e.g. two hospitals, primary care providers, public health authorities). Methods: semi structured interviews
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reconstructions of quantum theory, indefinite causal structures, extended Wigner's friend scenarios, and quantum reference frames. The team is embedded within the Quantum Optics, Quantum Nanophysics, and Quantum
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strong background and experience in performing advanced high-resolution TEM. Experience in performing such characterisation on 2D materials and a deep understanding of their structure-property
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CRISPR/Cas9 gene editing are desirable. Previous experience of in vivo models would also be advantageous. Project summary Serine proteinase inhibitors (serpins) are a superfamily of structurally similar
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methods with in situ characterisation techniques such as in situ Raman spectroscopy (including SHINERS) and electrochemical mass spectrometry. This combination will be used to investigate the structure
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optimize epitaxial growth of complex oxide nanostructures, especially ferroelectrics, via solid-phase epitaxy (SPE) Perform thin-film and device characterization across structural (XRD, AFM, SEM, XPS, TEM
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structural models and compute electronic and vibrational properties. Develop and train neural-network or other machine-learned interatomic potentials to enable large-scale molecular dynamics (MD) simulations