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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 7 days ago
Cardiometabolic Disease 103022 Full time 39 hrs./week Neuherberg near Munich At Helmholtz Munich, we develop groundbreaking solutions for a healthier society in a rapidly changing world. We believe that diverse
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, electrophysiology, and live-cell imaging Apply and further develop methods for high-resolution and super-resolution fluorescence microscopy Analyze in vivo models, particularly using slice electrophysiology, protein
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neurological populations. Additional responsibilities may include mentoring BSc and MSc students and taking on limited teaching duties. Would you like to learn more about what it’s like to pursue a PhD at
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carries out internationally high-quality life science research and provides education within various university programmes. Research revolves around four main themes: Cell & Systems Biology, Neurosciences
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services and system upgrades for large-scale wind generation and electric mobility in Iceland. Position Description The position is funded through the SIF PhD Support Fund (Reykjavík University) for one year
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this pathogen to cause infections, survive attacks by innate immune cells (macrophages and neutrophils), and develop resistance to antifungal agents (see selected publications below). The PhD student will learn
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, including pipetting, cell culture, and molecular biology techniques, or a willingness to acquire these skills; - a proactive, independent, and collaborative working style. Experience with organ perfusion
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following research directions. 1. Polymer-based artificial cells, biointeractive materials, and metabolic coupling We will develop soft polymer capsules and microgel artificial-cell platforms with tunable
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how AI is changing work. Its projects examine what happens when generative and agentic AI enters leadership, everyday workflows, team communication, professional learning and career decisions. Across
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computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and