102 computer-programmer-"Multiple"-"Humboldt-Stiftung-Foundation"-"U"-"U.S" "Prof" Postdoctoral positions in Sweden
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research plan) Strict compliance with the MSCA mobility rule that the researcher must not have resided or carried out his/her main activity (work, studies, etc.) in the host organisation's country
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at least 1 million DNA barcodes. The project involves collaboration with a computer vision lab at Linköping University, focused on developing AI-assisted techniques for picking out specimens for genome
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We are pleased to announce a postdoctoral position focusing on developing advanced computational techniques to reduce the complexity in simulating lake physical dynamics at scale. Borrowing from
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and independently plan scientific research, as well as collaborate with other project members. Excellent proficiency in English, both oral and written. Meriting Qualifications: Experience in
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This postdoc project aims to address a critical challenge in quantum computing: errors in superconducting qubits caused by cosmic radiation, which cannot be corrected using existing methods
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, advanced light and electron microscopy, and computational life sciences Guidance on relocating and settling in KTH and in Sweden Qualifications Requirements A doctoral degree or an equivalent foreign degree
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no more than 20% of the total workload, may be included among your duties. Qualifications Anyone who has a doctorate in forest product, building technology, industrial engineering, informatics, equivalent
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education and to the educational programmes at MDU. A broad range of topics and perspectives are thereby possible (workplace-based learning, simulation in professional education, student characteristics etc
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– at Umeå University, which hosts a Centre of Excellence focused on living systems under stress. IceLab brings together early-career and senior researchers from the life sciences, computer science, physics
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application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description