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on a single cell level. This includes single-cell RNAseq datasets and analysis tools, large high-dimensional flow cytometry phenotyping panels and a collection of transgenic mouse models specifically
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– Organoids as host model (Bartfeld lab) Immune–Metabolic Adaptation in Pregnancy: Implications for Offspring Health (Diefenbach lab) Variant Surface Antigen Expression in chronic Plasmodium falciparumcarriage
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Karlsruher Institut für Technologie (KIT) | Karlsruhe, Baden W rttemberg | Germany | about 1 month ago
description: The focus of the RTG is on the development, characterization and modelling of novel, multi-phase composite systems capable of withstanding temperatures substantially beyond 1300 °C and harsh
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, and regulate plasticity. We use cutting-edge methods (multi-omics, single cell, live cell imaging, modelling, chromatin biochemistry as well as various developmental, stem cell, and cell biology systems
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Modelling" (department “Geodesy”), we are looking for a: Reference Number 10727 We offer a position for the research project 'Advancing the understanding of glacial isostatic adjustment in Antarctica' (ANT
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experiments utilizing state-of-the-art approaches such as: Animal models of metabolic diseases and atherosclerosis Flow cytometric analysis of immune cell populations from murine tissues using full spectrum
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Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig | Leipzig, Sachsen | Germany | 3 months ago
Doctoral Candidate in computational neuroscience. (m/f/d) The position The position is part of an international collaborative effort (4 labs) to test computational models of episodic and spatial memory by
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, specifically battery prototypes. What you will do Multiscale modelling to better understand RFB behavior and identify optimal hierarchical shaped pore- and electrode-structure to encounter optimum electrolyte as
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electrolysis and fuel cells (SOEC and SOFC). By combining numerical modeling with data-driven approaches, you will identify optimized operating conditions and strategies to improve both steady-state and dynamic
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modeling with data-driven approaches, you will identify optimized operating conditions and strategies to improve both steady-state and dynamic performance in fuel cell (biogas) and co-electrolysis