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skills in written and oral English are a prerequisite. English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd
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Qualifications: Completed doctoral studies – PhD in bio-resource technology, practical implementation of Machine Learning, or a related field. Strong knowledge of Food security theory. Understanding of principles
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. status in Marketing from an AACSB or regionally accredited program. The candidate’s academic preparation should qualify them to teach in one or more of the following areas: Marketing Research, Principles
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, earth or energy. Learn more at www.hds-lee.de . Institute specific promise here. We are looking to recruit a PhD position – Co-regulation structures for large-scale single-cell transcriptomics – within
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at both the undergraduate and graduate levels, as well as advisement of undergraduate Honors theses, master’s theses, and PhD dissertations. Joining a robust faculty active in the scholarly community
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candidates to teach at the introductory, intermediate, and advanced levels, in both classroom and laboratory settings. We are seeking candidates who can demonstrate their experience in teaching or working with
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for farm-farm interaction Development of coupled LES and aero-elastic models using the actuator line method Analysis and design of wind farm control through LES and machine learning Scientific publication
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to For details, see: https://www.gradschool.physics.uni-koeln.de/applying/applying-for-phd-in-bonn-or-cologne . Accommodation Accommodation is available through the student union ("Kölner Studierendenwerk
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. Appointment procedure: To apply for this position, you need to submit identical applications both the CERN and the system: Apply online here to CERN portal: https://careers.cern/jobs/marie-curie-phd-position-in
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LIST? Check our website: https://www.list.lu/ How will you contribute? The Post-Doc researcher will develop, implement, and apply advanced ways in inverting a radiative transfer model for forest trait