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The Faculty of Science invites applications for a POSTDOCTORAL RESEARCHER IN MACHINE LEARNING FOR NATURE CONSERVATION starting from August 2025 or as agreed. The Postdoctoral Researcher will be
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. We require the candidate to have documented experience in either large-scale genomics data analysis with computational or approaches/biostatistics, or machine learning/deep learning. Experience with
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of large-scale genomic data sets is a requirement for this position. Experience with data integration, machine learning, network science, cancer biology, and/or gene regulation is considered an advantage
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strong publication record and a solid background in computational RNA biology, particularly in alternative splicing regulation, are our top priorities. Expertise in machine learning and human omics data
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, are our top priorities. Expertise in machine learning and human omics data analysis is highly advantageous. The ideal candidate should demonstrate a high level of independence while also valuing
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dysfunction. An ensemble of multi-scale computational approaches (molecular dynamics simulations, quantum chemistry, machine learning) are applied to study the mechanistic aspects of biomolecules in great depth
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. The project will focus on using and extending deep learning-based approaches developed within the group to integrate bulk multi-omics cancer data. The Kuijjer group, established in 2018 at the University
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researchers (for further information, please see: https://blogs.helsinki.fi/viikki-postdoc/ ). Application and selection procedure To apply, please send the following documents in a single pdf file: * A letter
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) for more information about the position. To apply, please send (in a single PDF document) a cover letter describing your previous research achievements and motivation to apply, your detailed CV including a
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Recruitment System via the "Apply now" button. Internal applicants (i.e., current employees of the University of Helsinki) submit their applications by using the Employee Login button. Please send your