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Field
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to integrate and analyze genomics, transcriptomics, proteomics, metabolomics, and microbiome datasets to uncover biomarkers, therapeutic targets, and mechanistic insights into complex diseases. The project
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Research (SGN), is seeking to fill the following position at its location in Frankfurt am Main, ideally starting on 01.01.2026: PostDoctoral Researcher in Ecological Networks and NCP supply (m/f/d) (Full
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lead sophisticated multimodal bioinformatics integration and analysis, collaborating daily with a diverse team of computational scientists and clinicians to translate complex data into actionable cancer
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on Compact Manifolds with Novel Feedback Coupling and Complex Network Optimization." Specifically, the selected candidate will address Research Objective (RO) (2) Investigate the dynamical phenomena of orbital
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in real time. The candidate will contribute to modeling interactions between physical and digital infrastructures to enhance energy management, transportation networks, and urban sustainability. Key
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hypothesis generation in finance / natural sciences / physical sciences, enhancing collaborative workflows in complex organizational settings. Qualifications: Applicants must have a PhD in Computer
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implement innovative solutions. He/she will contribute to the development of novel concepts and proposal writing, while efficiently addressing complex challenges. Responsibilities will include writing reports
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; Woolhouse et al., 2012). The main activity of the recruited Post Doctoral fellow will be to explore at larges scales (e.g. biomes, ecosystem, watershed, virus/host taxonomy …) the complex molecular
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of the Microverse” (https://www.microverse-cluster.de/en/# ), the CRC/Transregio 124 “Pathogenic Fungi and Their Human Host: Networks of Interaction” (https://www.funginet.de/willkommen.html# ) funded by the Deutsche
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emphasize the different kind or levels, known as layers, of connections between the elements of the network and the interactions between these levels as well. In order to capture the structure and complexity