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at least 1 million DNA barcodes. The project involves collaboration with a computer vision lab at Linköping University, focused on developing AI-assisted techniques for picking out specimens for genome
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for estimating measures of mutational robustness. These measures could include the relative proportion of amino acid changing mutations per codon, and the proportion of changes that result in a radical versus
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courses equivalent to at least 60 credits in a mathematical subject and at least 30 credits in either numerical analysis or computer Selection The selection among the eligible candidates will be based
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include Drive research projects which include analysis of tissue images from multiplex immunofluorescence, spatial proteomics and transcriptomics Drive development of deep learning and computer vision tools
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, computer simulations and various statistical approaches to increase understanding of the ecological and evolutionary processes that underlie speciation and ecological diversification in the context
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will combine state-of-the-art computer vision, modeling and archived specimens to determine biotic and abiotic factors driving spatial variation in molt phenology. It will use museum genomics to recover
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technologies like normalizing flows, graph neural networks, and transformers to represent distributions over trees, to improve MSC estimation. These technologies have shown significant improvements in
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identify systems-level mechanisms in cancer that can be used to uncover new biomarkers, drug targets, and paths to drug resistance. The long-term goal of our lab is to enable computer-aided design of
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new methods. Good computer skills. Additional qualifications for the position are: Documented theoretical or practical experience in structural biology and/or mass spectrometry. Experience of project
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having passed exams in areas relevant to the subjects of image analysis and machine learning with a minimum of 90 higher education credits. Relevant courses include, for example, image processing, computer