133 computer-science-programming-languages-"U.S" "INSAIT The Institute for Computer Science" Postdoctoral positions in Sweden
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requirements A PhD or equivalent research qualification within the subject of the position. Very good oral and written proficiency in English. Solid background in small-molecule characterization. Ability to work
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a postdoc fellow at the AMBER programme you will get unprecedented medical, biological, and methodological capabilities, with a profound potential impact for Europe’s next generation of research and
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/councils, EU framework program or industry. Qualifications To be eligible for this postdoctoral position, you must hold a PhD in Structural Engineering, Civil Engineering, or a closely related field, with a
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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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-leading research in several fields. Notably, the groundbreaking discovery of the CRISPR-Cas9 gene-editing tool, which was awarded the Nobel Prize in Chemistry, was made here. At Umeå University, everything
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case of different interpretations of the English and Swedish versions of this announcement, the Swedish version takes precedence. Blekinge Institute of Technology, BTH, embraces diversity and welcomes
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of the Wallenberg Centre for Quantum Technology (WACQT, http://wacqt.se ). The core project of the centre is to build a quantum computer based on superconducting circuits. You will be part of the Quantum Computing
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Biology The Department of Laboratory Medicine At present, the institute has about 340 employees and approximately 450 million SEK in total assets. This open postdoc position is in association with Professor
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at the AMBER programme you will get unprecedented medical, biological, and methodological capabilities, with a profound potential impact for Europe’s next generation of research and researchers. When
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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