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Field
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image analysis to extract cellular morphology and spatial features. Analyze multi-modal datasets combining transcriptomics, chromatin accessibility, imaging, and electrophysiology. Develop and apply
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image analysis to extract cellular morphology and spatial features.Analyze multi-modal datasets combining transcriptomics, chromatin accessibility, imaging, and electrophysiology.Develop and apply machine
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related field. You bring a strong analytical background and experience with optimization models and quantitative analysis methods. Familiarity with urban energy systems, energy infrastructures, or spatial
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methods for single-cell data analysis (tools developed by the team : https://github.com/cantinilab ). Single-cell high-throughput sequencing, extracting huge amounts molecular data from a cell, is creating
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the occupational risks faced by inhabitants of the Roman Empire influenced their choice of preferred cults. The main methods used in the project include spatial analysis, predictive modelling, statistics, and the
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several types of spatial growth patterns which correlate with therapy efficiencies and overall survival (Vermeulen et al. J Pathol. 2001 , Nielsen et al. Mod Pathol. 2014 , Baldin et al. J Pathol Clinical
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 15 days ago
plan comprises: Research and review of the relevant scientific and technical literature for the project; Survey and analysis of shading solutions, with particular emphasis on prefabricated systems
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analysis and spatial data processing. Experience in applying for research funding or a demonstrated ability to write high-quality grant proposals. Demonstrated experience in coordinating data collection
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a unique opportunity to join a world-class team dedicated to understanding and advancing the field of cancer immunology through innovative spatial analysis of the tumor microenvironment. Requirements
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clinical trials. Key Responsibilities Perform end-to-end analysis of single-cell RNA-seq, multiome, and spatial transcriptomics datasets from patient samples. Integrate multi-modal genomic, clinical, and