67 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of St" "St" Fellowship research jobs at Nature Careers
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academic transcripts. Eligibility & Funding Positions: Researchers who meet the conditions on: https://tubitak.gov.tr/en/scholarships/international/research-scholarship-programs/2232-b-international
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(EHR), health information exchanges, and data analysis software. Experience with health IT innovation, including working with artificial intelligence, machine learning, telemedicine, or mobile health
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: https://ragoninstitute.org/yu/ and https://ragoninstitute.org/lichterfeld/ Job Duties: Responsible for the design, execution, and interpretation of experiments; Interfaces with collaborators to design
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your CV and academic transcripts. Eligibility & Funding Positions: Researchers who meet the conditions 4.1.1-4.1.4: https://tubitak.gov.tr/sites/default/files/2024-11/2232-Guide-EN.pdf https
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of January 2026 on the dedicated platform (https://ibsafoundation.poliresearch.com/ ) and shall provide the following additional documents as separate files: Curriculum Vitae List of peer-reviewed publications
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decisions are anticipated to be made by April 1st, 2026. Applicants must apply online at: https://www.princeton.edu/acad-positions/position/40521 Applications must be completed by January 31, 2026 at 5:00 PM
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computational pipelines for multiplex imaging, spatial transcriptomics, single cell RNAseq, and multi-omics data integration. Lead graph-based network and machine learning analyses of tumor immune
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(EHR), health information exchanges, and data analysis software. Experience with health IT innovation, including working with artificial intelligence, machine learning, telemedicine, or mobile health
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of this programme. The profile PhD in computer vision, computational biology, physics or a related discipline Demonstrated expertise in image analysis and working with large-scale imaging datasets Strong expertise in
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expression, cell state-specific regulatory programs, and clinical outcomes. Related projects will include: Develop and apply statistical or machine learning approaches to model the effects of common and rare