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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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captured from UAVs. The research will address the design of AI models capable of combining heterogeneous sensor modalities, including RGB, thermal, LiDAR, acoustic arrays, GPR, and X-ray backscatter
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microscopy, spatial transcriptomics and in vitro and in vivo models to study brain tumor cell-cell interactions and the organization of the cancer ecosystem. We strive for highly collaborative and inclusive
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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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the molten salt NaAlCl4 electrolyte chemistry by among others in situ Raman probe spectroscopy to investigate spatially resolved compositional changes during charging/discharging of the battery, and
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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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, 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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immediately, depending on visa status and requirements. Group or Departmental Website: https://med.stanford.edu/matteo-mole.html (link is external) https://www.devo-evo.com (link is external) How to Submit
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) and induced pluripotent stem cells (iPSc) as model systems, as well as human brain tissue. In the future, and as our research program advances, we will expand our toolkit to also include mouse work and
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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