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models for the analysis of complex longitudinal data with repeated measurements, especially in neuroimaging and interventional neuroscience. Key topics include: linear and nonlinear mixed models with fixed
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to develop tools that (i) integrate multi-modal data, (ii) model gene regulation at increased genomic and cellular resolution, (iii) refine the analysis of genome-wide regulatory networks. The group applies
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(e.g., mathematics, physics or computational social science), with a strong track record in network science (or complex networks / social network analysis); Excellent writing skills and proficiency
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at increased genomic and cellular resolution, (iii) refine the analysis of genome-wide regulatory networks. The group applies these tools to study various cancer types, including ovarian cancer. For more
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The postdoctoral researcher will join the "Network Dynamics & Computations" team led by Srdjan Ostojic and develop research projects on modeling neural circuits and their role in behavior. The work will focus
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, microfluidic analysis, CRISPR screens and functional assays Use synthetic biology approaches to harness NK cell functions Apply bioinformatic analysis and integration of patient data Collaborate within network
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. The candidate will also be able to network with the broader regional team of the VectorEd Network and its technical experts at the CDC since this project is supported by a Cooperative Agreement from the Centers
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collect the data needed for network resilience analyses. Conduct a comprehensive resilience analysis on a portion of the Italian road network by applying and testing the developed tool. Where to apply E
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of reaction pathway exploration methods based on chemical and microbial reaction networks ・Design and implementation of computational tools for reaction systems ・Construction and analysis of mathematical models
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the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Development of a multi-layer network model