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Field
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. Project details In this project we aim to develop graph deep learning methods that model spatial-temporal brain dynamics for accurate and interpretable detection of neurodegenerative diseases
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2026. The UNFoLD lab specialises in the experimental measurements, analysis, and modelling of unsteady vortex-dominated flow phenomena, with applications in bio-inspired propulsion, wind turbine rotor
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. Spatial Transcriptomics: Application of Spatial Transcriptomics to new patient samples with the aim of validating the molecular signature of the cell populations identified by the AI using Giemsa morphology
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changing spatial regulations. You will also help design economic decision-support tools to inform more inclusive and evidence-based marine policy. Your duties and responsibilities include: analyzing
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Directed Energy Deposition (DED) process for metallic components. The PhD candidate will focus on edge computing and the application of AI for data analysis and for identifying correlations with ground truth
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and accepted to the PhD program at Stockholm University. Project description Project title: “Deep learning modeling of spatial biology data for expression profile-based drug repurposing”. A new exciting
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approach including empirical data analysis, experiments, and theoretical modelling to develop science-based management strategies for the restoration of woodland ecosystems. We will collect, and collate from
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analysis techniques to explore socio-economic impacts of an AMOC weakening. You will get the opportunity to connect to the IMAGE model, an integrated assessment model that couples our climate system to human
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emerging types of national emergencies and evaluate their spatial and operational implications. This will include an analysis of UK population distributions, terrain, infrastructure access, and airspace
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actors involved in managing public space? How can they adapt to dynamic societal needs and increasing spatial interdependencies? What institutional models promote more efficient, equitable and resilient