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Field
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eager to explore the interface between cell wall mechanics and soil physical properties applying spatial gene regulation to understand root biomechanics. This position is ideally suited to a highly
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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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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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“Enhancing Machine Learning Approaches for Spatially Dependent Data in Fisheries and Environmental Research” (CMAT, University of Minho), reference 2024.15617.PEX, financed by national funds through
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activities through field surveys and data analysis; 4. collection, processing and analysis of samples for environmental DNA (eDNA) aimed at species detection and spatial distribution assessment; 5. performance
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genomics is considered an advantage Understanding metastatic potential and tumor immunobiology at the single cell and spatial level In this project we use all-things-omics, with short and long read
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in Spatial Omics and Multi-Modal Data Integration Duties & Responsibilities: Develop computational and machine learning methods for spatial omics data (spatial transcriptomics, spatial proteomics
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to sign the contract. More information is available on: https://www.dges.gov.pt/pt/pagina/reconhecimento . Workplan and the objectives to achieve: This work aims to develop a numerical approach based on
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available1Company/InstituteCentre d'études spatiales de la biosphèreCountryFranceCityTOULOUSEGeofield Contact City TOULOUSE Website https://www.cesbio.cnrs.fr STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn
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for gene regulatory networks, single-cell multi-omics integration, spatial omics, and variant effect mapping in complex disease. Strong method/tool dev experience required (Python/R, ML/stats