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Field
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. This postdoctoral fellowship in the Arlotta lab involves computational analysis of large, multimodal single-cell datasets, as part of a collaborative project which also aims to generate AI and machine learning models
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and single-cell omics (transcriptomics, proteomics, epigenomics, metabolomics, meta-transcriptomics, etc.) data. Independently carry out computational and bioinformatics analysis for large-scale spatial
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distributed sensing technologies. The role will focus on distributed acoustic sensing on pre-existing telecom fiber networks, mobile sensing using vehicle fleets, multi-modal data fusion, and structural system
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of multi-modal datasets, including MRI/fMRI, behavioural and audio data Develop reproducible statistical and machine learning analysis pipelines Support grant writing, ethics submissions and project
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focus on distributed acoustic sensing on pre-existing telecom fiber networks, wavefield analysis, structural and geophysical interpretation, multi-modal data fusion, and lab-scale and field experiments
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modalities, including: Single Base Substitutions (SBS) Insertions and Deletions (Indels) Structural Variants (SVs) Copy Number Alterations (CNAs) Other genome instability features. Perform mutational signature
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artificial intelligence and machine learning. The postdoctoral fellow will contribute to the development of a comprehensive, multi-modal framework for predicting and managing cardiovascular disease by
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for analysis and publication. They will also contribute to the development of new prospective clinical studies, including the establishment of standardized clinical databases and biospecimen collection pipelines
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multiple modalities of medical imaging (e.g. fundoscopy images, OCT scans, MRI, CT, X-ray and digital pathology). We bridge the gap between machine learning research and clinical practice through fruitful
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communities, testing dietary and other control modalities, as part of the learning experience. This will include various culture methods, nucleic acid extraction, PCR methods, sequence analysis