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available from 1 September 2026 or later. You can submit your application via the link under 'how to apply'. Title PhD candidate in plant computational biology Research area and project description Applicants
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Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position
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information and useful links about postdoctoral fellowship training positions at the NIH can be found at https://www.training.nih.gov/resources/faqs/postdoc_irp. DHHS, NIH, and NICHD are equal opportunity
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at the University of Oldenburg and involving national and international partner institutions. As a diverse and international team of scientists from biology, physics, chemistry, computer sciences and social sciences
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of carbon with concrete structures, and the reduction of carbon emissions linked to the construction sector. Your profile This project calls for an open character with a strong knowledge on concrete
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activities and the preparation of data Contribute to teaching The doctoral candidate will be a member of the Department of Humanities and the Research Institute of Multilingualism and participate to its
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role Conduct research on data quality and long-term reliability in smart sensor systems for industrial monitoring Develop methods, models, and computational tools for sensor data validation, anomaly
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technologies, as they define when information is processed, transmitted, or measured. As data rates continue to increase (e.g., 5G/6G and terabit optical links), the required timing precision becomes extremely
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PhD scholarship in synthesis and experimental studies of the phase behavior of ABC-miktoarm star ...
the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's
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morphology to directly link gene-regulatory cell states to functional neuronal phenotypes. This ambitious project integrates wet-lab experimentation with advanced computational analysis, and is ideal for a