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Join us at the Department of Electrical and Computer Engineering at Aarhus University for a postdoctoral position focused on deep learning based analysis of remote sensing data for groundwater
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Job Description Are you experienced in WGS data quality control and analysis from bacterial isolates? Do you have a strong interest in genomics and antimicrobial resistance (AMR)? The Research Group
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mathematical, statistical, and machine-learning-based analysis of complex data sets, such as hypothesis testing, supervised/unsupervised learning, linear models, etc. Experience with atlas-scale single-cell data
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studies and both qualitative (e.g. interviews or ethnographic methods) and quantitative data analysis. This postdoc is an opportunity to collaborate in an international Nordic project with external partners
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with colleagues on your project, where you will receive supervision and guidance. The candidate's main responsibility will be the bioinformatic analysis of omics data. You will do so in a project focused
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project activities. Key responsibilities include: Investigation into the GEUS sediment archive to extract information on the properties of subglacial sediments deposited by past ice streams. Analysis
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Postdoctoral Researcher in Natural Language Processing and Digital Humanities (18 months, full-time)
Latin intellectual traditions. The project combines intellectual history with computational text analysis to examine large, historically complex corpora spanning multiple centuries and languages. The core
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Postdoc position in method development in human statistical genetics, with a focus on classificat...
University. Dr Speed's research involves developing statistical methods for better analysing data from genome-wide association studies (GWAS), with a particular focus on improving our understanding of human
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Job Description Are you passionate about data science and X-ray experiments? If so, this position might be perfect for you. We are seeking a data scientist to advance our analysis of complex data
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University. Dr Speed's research involves developing statistical methods for better analysing data from genome-wide association studies (GWAS), with a particular focus on improving our understanding of human