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an outstanding and ambitious postdoctoral researcher in computational biology to pioneer understanding and modeling of tissue architecture using single-cell and spatial transcriptomics data. The focus will be
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candidates able to conceptualize and statistically or mathematically link processes occurring at different spatial or temporal scales, or across levels of ecological organization (e.g., upscaling of microbial
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theory, network science, resilience theory, disruption modelling, mobility systems, urban analytics, computational social science, agent-based modelling, causal inference, or spatial data science
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the implementation of a cognitive agent embodied in a humanoid robot Unitree G1 which will collaborate with a human partner to solve a spatial problem (e.g. 3D puzzle). The tasks to be carried out are: (i) scene
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cognitive agent embodied in a humanoid robot Unitree G1 which will collaborate with a human partner to solve a spatial problem (e.g. 3D puzzle). The tasks to be carried out are: (i) scene understanding
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power. The work involves quantifying conflicts and synergies between different sustainability goals in the forest landscape using simulation and optimisation. The analysis will be based on spatially
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. A central goal is to integrate spatial imaging data with time-resolved molecular measurements, while incorporating physical and biological constraints directly into the model architectures
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focuses particularly on understanding how the tumor microenvironment (TME) undergoes architectural remodeling – including changes in extracellular matrix composition, tissue mechanics, and spatial
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focuses on in situ formation of organic electronic materials in living environments, enabling new ways to create soft, adaptive, and spatially precise interfaces with biological tissue (see, for example