31 phd-in-gis-and-remote-sensing Postdoctoral positions at Aarhus University in Denmark
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, a large initiative funded by the Danish Ministry of Foreign Affairs and managed by Danida Fellowship Council. Ethio-Nature aims to optimize the use of machine learning and remote sensing to site
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. CHC values a healthy work-life-balance and offers work conditions where flexible hours and some amount of remote work is possible. Qualification requirements Applicants should hold a PhD or equivalent
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: The successful candidates will work primarily with the AarhusNLP Group at the Center for Humanities Computing, Aarhus University. The Center offers flexible working arrangements, including options for remote work
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sediment core laboratory. Qualifications The qualified applicant will have a PhD degree in geoscience or related fields. Candidates should have a background in high-resolution marine seismic and acoustic
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the strength of heterogeneous materials”. Your profile You hold a PhD in engineering, mathematical modelling or similar. Scientific challenges excite you and you feel comfortable talking to people with different
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of junior researchers You will report to Group Leader Thomas Kim. Your competences You have academic qualifications at PhD level, for example, within the following areas: Required: PhD in neuroscience
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. At the department we are approx. 670 academic employees, 500 PhD students and 160 technical/administrative employees who are cooperating across disciplines. As a postdoc, you will be working at Aarhus University
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PhD in political science or another field relevant to the project such as computational social science, communication studies, media studies, or sociology. In addition, applicants are expected
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employees, 500 PhD students and 160 technical/administrative employees who are cooperating across disciplines. You can read more about the department here and about the faculty here . The project is based
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with a focus on water use efficiency by validating models using the collected high-time resolution datasets. Linking digital sensing technology with model platforms (e.g. APSIM) using data fusion