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and Planning division. SMoG is an internationally oriented research group investigating how urban form influences urban life through rigorous spatial analysis and empirically validated models. Its long
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prediction Integrating various types of spatial single-cell data, such as gene expression and protein expression data Developing image analysis methods for histopathological specimens using AI-based models As
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and damaging storms, and cascading risks such as bark-beetle outbreaks. Forest owners and regional authorities need spatially detailed, timely information on where climate stress is emerging, and which
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by Digital Futures to map urban air temperature at high spatial and temporal resolutions. Cities face rising urban heat, both from sharp spikes and prolonged hot periods; however, producing these high
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to have a background and a strong interest in one or several of the following areas: glial biology, molecular neuroscience, single cell and spatial genomics, gene delivery, animal models of CNS injury
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. The primary objective of this PhD project is to develop adaptive statistical models for marked spatial and spatio-temporal point processes. Many real-world systems exhibit substantial spatial heterogeneity and
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or biophysical approaches to measure mechanical properties of biological materials. Experience with spatial transcriptomics, in situ sequencing, or single-cell genomics. Skills in biological modelling
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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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, audio, and sensor data. These AI models move beyond conventional predictive and purely data-driven approaches by seeking to capture the underlying causal, spatial, temporal, and semantic relationships
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, energy systems, electrification, resilience, equity and sustainability, combining large-scale empirical data, modelling, simulation and systems analysis. In Energy, Environment, and Systems PhD program