14 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at SciLifeLab
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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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systems, and machine learning. While the initial focus of the position is on this project, we offer significant opportunity for the applicant to develop their own independent research trajectory in
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bioinformatics, with a particular emphasis on performing analysis of high-dimensional data, which can be sequencing and/or imaging-based. Experience working with AI and machine learning approaches are considered a
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and/or application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. Tasks The tasks include primarily leading and conducting research
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investigation. Being part of a larger collaborative project, the postdoctoral researcher will be involved into discussions within a broad range of fields including computational, medicinal and organic chemistry
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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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evidenced by recent publications in e.g. Nature Biotechnology – and also provides a stimulating environment for learning computational biology. The successful applicant will in furthermore receive training
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across Sweden and beyond. At NGI, you will be part of a dynamic environment with access to a broad range of instruments, high throughput automation, and strong computational expertise (https
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Starting Grant from the European Research Council and a DDLS Fellowship from the SciLifeLab and Wallenberg Swedish program for data-driven life science. The successful candidate will be working within
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position is part of a five-year research program funded by the Wallenberg Foundation, which aims to develop and apply computational tools to understand the evolution of biodiversity (see https