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place technologies and to develop digital twin algorithms to assist clinicians in developing treatment plans. Analyzes complex sensor data, works with a multidisciplinary team to develop health digital
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RNA-seq data for testing and validation. Perform data analysis on large-scale RNA-seq data in pediatric cancer. This may involve the analysis of both scRNA-seq, bulk RNA-seq as well as new single-cell
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the application of advanced data science approaches to explore large-scale clinical datasets extracted from electronic health records, with the goal of understanding how inflammatory processes intersect with
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We are seeking an experienced Earth Observation and Modelling scientist who has knowledge of, and expertise in analysing Earth Observation data including Satellite Remote Sensing together with Soil
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researcher will work directly with Dr. Fei Chen on analyzing data from genetic association studies, which includes GWAS and/or sequencing data from large-scale genetics consortia such as the RESPOND Study, UK
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involves interdisciplinary research on the development and evaluation of large language models for low-resourced langauges, primarily focusing on Danish and other Nordic languages. The research will focus
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through both academic and non-academic channels The project is multidisciplinary and in the project you will join a large team of researchers from different universities and disciplines. For this reason it
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for projects examining questions in cardiovascular disease using extremely large data sets comprised of routinely-collected clinical data · Developing and maintaining requirements for access to Truveta Data and
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large-scale whole genome and whole exome sequencing, RNA-seq, ATAC-seq, proteomics, and metabolomics data in a well-established cohort of childhood cancer survivors with clinically ascertained deep
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complex challenges. Desirable Experience in HE/industry collaborations. Peer-reviewed publications in machine learning, language modelling, computer vision, or applied AI. Familiarity with large, complex