79 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"I.E" positions at Aarhus University
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geological interpretation of large-scale EM data for groundwater mapping. Teaching and training Ethiopian partners and students in EM methods, data processing workflows, inversion software, and geological
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Electrophysiological characterization of muscle fiber excitability (in collaboration with the research group) In vivo studies using animal models of neuromuscular disease Integration of molecular and transcriptomic data
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wide variety of services for international researchers and accompanying families, including a relocation service and an AU Expat Partner Programme . You can also find information about the taxation
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, they will be required to acquire Danish-language proficiency within approximately two years. The application must be submitted in English. Further information For further information about the position
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include psychiatric disorders as well as clinical and social outcomes, but specific tasks may depend on applicants. The positions will generally involve various data analyses using Danish register data and
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environment. There is overall a close collaboration between the academic and the clinical staff at DCPT. About the research project The research project of this postdoc will be based on follow-up data from a
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for the position in question is a broad ranging of techniques ranging from spatial and single-cell analyses to classic methods like histology cell culture and Western blotting. Data handling through bioinformatics
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Development for more information. About you To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria Fluency in English Strong skills in
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conditions for art and culture. Possible focal areas include AI and algorithmic creativity, digital media aesthetics, data-driven culture, new forms of the dissemination of art, literature, theatre and music
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will