176 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"P" Postdoctoral positions in Denmark
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paths at DTU here . Further information Further information may be obtained from Professor Xenofon Fafoutis and Associate Professor Luca Pezzarossa . You can read more about ESE at www.compute.dtu.dk
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to be held after the application deadline. Further details will be communicated to shortlisted candidates. Further information Read more about our recruitment process here The appointment process at
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with a background in either communication, journalism, political science, sociology, law, economics, or computational social science, as well as an interest in how AI transforms the media and information
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for international researchers and accompanying families, including assistance with relocation and career counselling to expat partners. Please find more information about the International Staff Office and the range
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. Further information Further information may be obtained from Associate Professor Xiaodong Liang (xlia@kt.dtu.dk ) and Professor Georgios M. Kontogeorgis ( gk@kt.dtu.dk ). You can read more about DTU
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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
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. We expect applicants to hold a PhD in a relevant field such as techno-anthropology, science and technology studies, human-computer interaction, human-robot interaction, digital health, anthropology
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research sections with around 350 highly skilled employees, of which approximately 50% are scientific staff. More information can be found here . We believe in encouraging inclusion, acceptance, and
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agreed upon with the relevant union. The period of employment is 2 years. You can read more about career paths at DTU here . Further information Further information may be obtained from Antonio Grimalt
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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