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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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. 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 heterogeneous environmental datasets Familiarity with spatial analysis, GIS, or geospatial data workflows. Experience with machine learning, modelling, or systems analysis approaches Interest in resilience
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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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highly motivated and talented PhD candidates to join our team in developing novel organic bioelectronic strategies for interfacing with the nervous system. The positions are based at the Laboratory