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incorporate methods that integrate: - Mendelian randomization and genetic instruments - Bayesian hierarchical models and Gaussian graphical models - Multi-layer data integration across tissues and omics
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the reliability of non-destructive testing and structural health monitoring methods The objective is to develop and validate a hybrid approach combining simulation and experiment to analyse the reliability
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Dynamics (MBD) department contributes, using computational methods, to the development of personalized microbiome-based strategies to aid in the detection, monitoring, treatment, and prevention of human
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Max Planck Institute for the Structure and Dynamics of Matter, Hamburg | Hamburg, Hamburg | Germany | 12 days ago
UFAST – Hamburg Research The program offers exciting research opportunities at ultra-intense electron and X-ray sources to directly observe atomic motions during primary events. The use of ultrafast
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to detect health risks to humans and the environment, to better understand pollutant dynamics, and to achieve a molecular-level understanding of complex systems. Project A major challenge facing our society
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Technologies, studies how this capacity for learning can become a lasting part of governance. Its doctoral projects address four connected themes: Anticipation and foresight: How can authorities detect emerging
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Chair of Biological Imaging 13.08.2026, Academic staff Join us in shaping the future of biomedical imaging We are looking for a curious and persistent PhD student (f/m/d) who wants to discover new
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infrastructures, social interaction, material objects and media technologies. In turn, knowledge is understood as a means to define and unlock such resources, while being one of the key resources of human culture
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Brandenburg University of Technology Cottbus-Senftenberg • | Cottbus, Brandenburg | Germany | about 2 hours ago
Energy Reform and Decarbonisation Biotechnology, Environment and Health Cognitive and Dependable Cyber-Physical Systems Objectives of the Graduate Research School: Promotion of outstanding young
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on massive datasets that exceed the capabilities of single-node systems. A key objective is the development of efficient iterative algorithms for training networks based on random features, which can be