64 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" Fellowship research jobs at Nature Careers
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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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: 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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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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opportunity to engage in collaborative research alongside Air Force scientists and engineers within the Air Force Research Laboratory (AFRL) , the Air Force Institute of Technology (AFIT) , the U.S. Air Force
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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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and expanding team. You’ll play a key role in our success through your code, publications, and strategic promotion of our work. * PhD in Computer Science, Biomedical Informatics, Machine Learning
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healthcare data. * A team player who thrives as a member of a highly functional cross-disciplinary team Preferred Elements * B.S, M.S., and/or PhD in Computer Science, Biomedical Informatics, Machine Learning
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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
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. This project will involve applying and evaluating statistical and machine learning models for data integration and interpretation. A strong foundation in statistical modeling will be essential for applications