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<span lang="en">Universitätsklinikum Hamburg-Eppendorf</span> | Hamburg, Hamburg | Germany | 14 days ago
Mercatorum, Rome. Our goal is to use spatial transcriptomics, digital histopathology and clinical data to understand the molecular basis of glomerular kidney diseases.
The position is jointly led by
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learning and statistics Apply for this job See advertisement About the position We invite applications for a three-year PhD Research Fellowship in probabilistic machine learning and statistics at the
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unknown substances in selected soil matrices; iii) assessing spatial patterns, persistence and transformation of contaminants where suitable data and samples are available; iv) using environmental
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spatial transcriptomics datasets, and of paired molecular and functional measurements (e.g., electrophysiology), as well as optimally leveraging integration with existing genomics datasets. The role is
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agricultural data with mass and nutrient balances, scenario modelling, and life cycle-based, techno-economic and socio-economic methods. Data will be drawn from research, statistics, literature and national
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geographic and historical information sources that are integrated into spatial analyses and statistical models. The work may also include laboratory analyses of soil carbon to provide a clearer understanding
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thesis work in complex systems, network science, agent-based modelling, transport modelling, urban analytics, resilience, computational social science, data science, machine learning, statistics
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reactive or based on fixed schedules, despite large spatial and temporal variability in sediment accumulation. This PhD project is part of a larger interdisciplinary research initiative aiming to enable a
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components. It addresses a fundamental challenge in industrial digitalization: the lack of formal, machine-interpretable representations that integrate structural, functional, spatial, and temporal
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retrievals from TGO and Mars Express alongside energetic particle and plasma measurements to characterise the atmospheric response to solar storm events. Martian aurora , investigating the spatial and