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Position Details Position Information Internal Posting? Posting Number SP005144P Position Title Postdoctoral Fellow - Forest Service Division/College College of Science Department Earth and Spatial
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research and applications of advanced spatial data analysis methods, data collection, and interpretation of remote sensing data, database system theory, mapping, spatial data distribution, development of new
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focuses on studying the socio-geographic transformation of society, its conditions, developmental tendencies, and spatial structure - with implications for societal organization and an emphasis
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, progression, and therapeutic response. This research is fundamental to advancing our knowledge of cancer and improving patient outcomes. See further information at the lab webpage: https://odin.mdacc.tmc.edu
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for high-dimensional datasets, including spatial bulk and single-cell RNA sequencing, mass and flow cytometry, and imaging data derived from both patient samples and experimental models. The overarching aim
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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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their spatial patterns in varying environmental conditions. The successful candidate will integrate to the research unit « Microbial Oceanography - LOMIC : http://lomic.obs-banyuls.fr » in Banyuls sur mer, and
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two years (July 2026 – June 2028) The Research Assistant will support federally funded studies focused on children’s development of small and large-scale spatial skills. This role includes administering
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Instituto de Geografia e Ordenamento do Território da Universidade de Lisboa | Portugal | 21 days ago
corresponds to a land occupation pattern characterized by isolated and discontinuous constructions in a predominantly rural or peri-urban territory, resulting in low density and spatial fragmentation
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datasets generated experimentally including but not limited to bulk and single cell multiomics sequencing, epigenomics data including ChIP-Seq and CUT&RUN, and imaging data including spatial transcriptomics