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-temporal-resolution bulk measurements (XAS, Raman spectroscopy, XRD), with particular emphasis on pair distribution function (PDF) analysis. Machine learning approaches will be used to support these analyses
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disciplines. Strong interest in physical metallurgy, thermodynamics, kinetics and phase transformations. Previous experience with phase transformations, SEM/EBSD, XRD, thermal analysis, modelling or scientific
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learning, materials characterisation or computational materials science. Previous experience with machine learning, computer vision, graph neural networks, Python, SEM/EBSD, XRD, image analysis
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knowledge of building materials and be fluent in English. The following work or research experience will be an advantage: experience with cementitious materials, XRD with Rietveld analysis, SEM, TG, DSC
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, and interface-related energy losses Fabricate and evaluate complete single-junction and, where relevant, semi-transparent perovskite subcells using PL/PL mapping, XRD, current–voltage measurements, EQE
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metallurgy, materials characterization, fracture mechanics, mechanical behaviour of materials, or a related field. Experience with one or more advanced characterization techniques such as SEM, EBSD, TEM, XRD
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or more advanced characterization techniques such as SEM, EBSD, TEM, XRD, SIMS or related methods is highly desirable. Experience with mechanical testing, fracture mechanics, hydrogen embrittlement research
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-based electrocatalysts is considered a plus. Electrolyzer testing, reactor design, electrochemical system optimization. Operando characterization such as XRD, Raman, FTIR/ATR-SEIRAS, XAS, and EQCM
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experience from PVD synthesis methods, like magnetron sputtering, plasma analysis, as well as thin film characterization techniques like XRD, SEM, TEM, or XPS is beneficial. Fluent English spoken and written
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proton transfer. For the Diffusion Processes Group we are looking for a PhD Student Your tasks You will prepare and characterize clay samples and oriented clay films using different methods (e.g. XRD