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of analyzing spatial and quantitative relationships in high content multiplex histopathology images Skills and experience of matched designs plus advanced statistical modelling of high-dimensional data, e.g
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Memorial Sloan Kettering Cancer Center | New York City, New York | United States | about 20 hours ago
Role Overview: We are seeking a highly motivated computational biology postdoctoral fellow to lead translational studies in single cell and spatial biology of gastric cancer immunology and evolution
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data analytical models to integrate spatial single-cell genomics and bulk (epi)genomics. Candidates with expertise in machine learning approaches for integrating WES and RNA-seq data, including somatic
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bioinformatics for a fixed term (CDD) Research Engineer (Ingénieur d’études) position within the team. Main Mission Mammary organoid culture and bioinformatic analysis of spatial transcriptomics data. Main
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spatial statistics, GIS and linear modelling to conduct desk-based analyses. In the field, the successful student will be trained in census methods, plant identification and experimental design. Training
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. https://eurosoho.eu/ FUTURA coordinates European modelling and infrastructure efforts to jointly develop and deploy a sustainable, aligned system for delivering future emissions scenarios, and climate and
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variables related to production-system typologies. - Develop, adapt, and apply quantitative models to assess livestock interventions, including process-based, biophysical, spatial, and integrated assessment
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This PhD aims to develop novel robotic monitoring and data processing methods to document changes in dynamic seafloor environments over large spatial scales. Motivation: Understanding how
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, feed composition, and variables related to production-system typologies. Develop, adapt, and apply quantitative models to assess livestock interventions, including process-based, biophysical, spatial
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This PhD aims to develop novel robotic monitoring and data processing methods to document changes in dynamic seafloor environments over large spatial scales. Motivation: Understanding how