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) Leading Researcher (R4) Positions Postdoc Positions Application Deadline 15 Apr 2026 - 00:00 (Asia/Nicosia) Country Cyprus Type of Contract To be defined Job Status Full-time Hours Per Week 40 Is the job
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learning on and off campus, including a growing emphasis on international experiences through short- and long-term study abroad. Students also have access to a robust program of applied research and outreach
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About the Opportunity About Northeastern: Founded in 1898, Northeastern is a global research (R1) university and the recognized leader in experience-driven lifelong learning. Our world-renowned
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team in the AI in Medicine Lab (www.aimlab.ca ). This position is based in the School of Biomedical Engineering. The successful candidate will work in the AI in Medicine Lab, applying machine learning
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mentor; and publication of research findings/scholarship during postdoc appointment period. Projects in the CEL group include comparative phylogenomic analysis of invertebrates, including Hawaiian insects
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learning environment for members of the Auburn University community. Please visit their website to learn more. Special Instructions to Applicants provide 3 references Quick Link for Internal Postings https
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, and a wide array of family-friendly and cultural programs to eligible team members. Learn more at: https://hr.duke.edu/benefits/ Equal Opportunity Employer: Duke is an Equal Opportunity Employer
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, network science, and decentralized machine learning. Welcome to read more about us at: https://liu.se/en/organisation/liu/isy/ks . For more information about working at ISY, please visit: https://liu.se/en
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 2 months ago
deep learning (x/f/d/m) Background With the project Deepcloud, we will leverage the machine-learning revolution to understand clouds and their role in the climate system. We aim to train a deep learning
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projects in data-driven nutrition, such as: statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome