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deep learning models (e.g., adapting methods in [6]) based on spatial cellular graphs constructed from these images to predict clinical outcomes. The research will be carried out using two
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Midlands Graduate School Doctoral Training Partnership | Loughborough, England | United Kingdom | about 2 months ago
administrative housing data, environmental indicators, and accessibility metrics — and apply advanced spatial methods such as multilevel modelling and geographically weighted regression to identify relevant
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, Digital Soil Mapping, Remote sensing (COPERNICUS data ecosystem), spatial data modelling, spatial analysis, neural networks, large scale datasets management with GIS, cloud computing, Big Data tools. You
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the molecular signatures of proteostasis loss and identify early markers of proteostatic failure. The role combines wet-lab spatial biology with computational approaches. You will work across models and scales
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involving both modelers and experimentalists. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR5253-MOUBEN-002/Default.aspx Work Location(s) Number of offers available1Company
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 3 days ago
), financed by national funds through FCT Workplan: The main objective of the fellowship will be to study the effects of heterogeneity in spatially-structured or host-structured multi-species models governed by
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to demonstrate knowledge of computational epidemiology, individua lbased simulation, spatial modeling of epidemics and other geospatial software. * Ability to show proficiency in data management. * Ability
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funding. Appointment Start Date: Fall 2025 Group or Departmental Website: https://hph.stanford.edu/careers/ (link is external) How to Submit Application Materials: Submit all application materials
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uses cutting-edge techniques including single-cell and spatial transcriptomics, proteomics, super-resolution microscopy, in vivo tracking, mouse models, and human patient tissues and iPS-derived cells
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to study chromatin and gene regulation in mammalian cells and human disease systems. Current ongoing projects include: statistical modeling and advanced machine learning/AI method development for predicting