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below and above its Néel temperature. The goal is to disentangle magnetic and structural contributions to electron diffraction data and to compare the results with measurements recorded using X-rays and
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requires an unprecedented computing and storage infrastructure. Processing these enormous amounts of data in a future data center, however, consumes significant resources. The fundamental challenge is: How
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supervised models on downstream regression tasks. A key challenge lies in effectively modeling the temporal nature of EO data. Since downstream applications require varying temporal resolutions and sequence
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different types of experimental data to establish reliable methods for identifying physically meaningful model parameters and assessing model accuracy under dynamic operating conditions. In a later stage
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of atomic-resolution structural and spectroscopic data. Your Profile Completed qualification to enroll for the Master thesis in Physics or Materials Sciences, with a good track record Willingness to
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quantitative methods, data analysis, or modeling is an advantage Very good command of written and spoken English with extensive vocabulary is required (at least C1 level according to the CEFR ), ideally
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relates to mesoscopic cortical dynamics, including oscillations and spatiotemporal activity patterns. By developing and applying advanced causal discovery methods for complex time-series data, you will
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two projects share the goal of developing stable and reliable perovskite-based tandem photovoltaics, while maintaining complementary scientific responsibilities. FZJ focus: automated and data-driven
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, post-doctoral position is available in the Guermonprez lab at Institut Pasteur Paris (“Dendritic cells and adaptive immunity” Unit, Immunology Department https://research.pasteur.fr/fr/department
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synthesis protocols Performing microscopy, scattering and rheology exepriments Analysis of experimental data Preparing scientific publications Participation in conferences in Germany and abroad to presenting