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workflows for the analysis of the local structure in soft functional materials (semi-crystalline and amorphous materials) using electron diffraction and electron energy loss spectroscopy methodologies. You
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Max Planck Institute for Biological Cybernetics, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 8 days ago
using functional MRI or ultrasound imaging. The Max Planck Institute for Biological Cybernetics conducts research that elucidates neurological processes across various organizational scales via
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are the first steps in development of endothelial damage, systematic analysis in pediatric population is not available up to date. You will apply innovative molecular and imaging methods to study the underlying
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for emerging material characterization. Core questions will be how structural motifs control ultrafast excitation dynamics and coherent electronic state evolution in chiral organic molecular semiconductors
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Completed university studies (Master) in Chemistry or related field Experience in metal-organic syntheses Experience in working with glove boxes and Schlenk methods Experience in X-ray diffraction techniques
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and flow chemistry Use of spectroscopic and spectrometric techniques as well as single crystal X-ray diffraction analysis to study molecular properties and molecular structures in solution and in
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glove boxes and Schlenk methods Experience in X-ray diffraction techniques and other structural characterizations Experience in working with radionuclides, esp. actinides and TRU elements is of advantage
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-clean, isotope-pure SOI chips by molecular beam epitaxy (MBE) for highly coherent Er quantum light sources, a key step toward scalable quantum networks. In particular, you will: Innovative growth
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heterogeneity; namely non-iid and domain shift, e.g. multi-modal data acquired by different scanners and imaging protocols. Publish and present scientific results at international conferences and high-impact
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: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong knowledge in Machine/Deep Learning with experience in discriminative models