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as soon as possible for one or two years on temporary term basis, with the possibility of renewal subject to satisfactory performance and funding availability. Applicants should possess a PhD degree in
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structure-adhesion relationships from characterization data Is Your profile described below? Are you our future colleague? Apply now! Education PhD in Material Science and Engineering or Material Chemistry
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will be appointed at Grade 6, spine point 30. Upon written confirmation that you have been awarded your PhD, your salary will be increased to Grade 7, spine point 31. If you require a visa to work in
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, you will be asked to indicate, which of the areas listed on the page above are of interest to you. The list includes positions covering Operator Algebras, Machine Learning, Analytic Number Theory
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offer a unique opportunity to contribute to high-impact research in drones, AI, and human-computer interaction. Successful candidates will join a dynamic and inspiring international research environment
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If you need help uploading your application or have questions about the recruitment process, please contact Arts HR support by e-mail: hsi@au.dk . Qualification requirements Applicants should hold a PhD or
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. amino acid and lipid metabolism), signaling pathways (e.g. mTOR and nutrient signaling), and systems-level regulatory networks that regulate basic T cell and dendritic cell biology and antitumor immunity
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computing, computer architecture, programming models and high performance computing. These are your qualifications: Must-haves: Completed doctoral/PhD studies in Computer Science or a closely related field
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if they were awarded the PhD degree up to 7 years before employment in the project. This period excludes intervals due to maternity leave, adoption leave, paternity leave, parental leave, or parental care leave
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 4 hours ago
involves processing and harmonizing high-resolution NASA EO data. Subsequently, we will architect and train an ensemble of deep learning and statistical models capable of identifying key wildfire drivers and