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encouraged to apply for the position. Aalborg University has a policy of parallel language use. International staff are encouraged, with support from the university, to learn Danish during their employment in
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, regardless of personal background and orientation, is encouraged to apply for the position. Aalborg University has a policy of parallel language use. International staff are encouraged, with support from
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The SDU Adaptive Intelligence Lab (ADIN Lab) (https://adinlab.github.io/ ) located under the Data Science and Statistics Section of the Department of Mathematics and Computer Science (IMADA
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, everyone, regardless of personal background and orientation, is encouraged to apply for the position. Aalborg University has a policy of parallel language use. International staff are encouraged, with
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Aarhus and Aalborg. The primary workplace will be in Aalborg, with regular activities in Aarhus. The expected distribution is ap-proximately 1–2 days per month in Aarhus and the remaining working days in
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carcasses for analysis. Your task will be to explore what we can learn from the beached bird data. Topics include the distribution of mortality in time and space, and among species; causes of death, as
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and inference for distributed AI agents, and how the realities of networked operation shape the design of adaptive, resilient intelligence. Finally, the research will address how groups of embodied
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. The position is a full-time position. The position is part of DTU’s Tenure Track program. Read more about the program and the recruitment process here . You can read more about career paths at DTU here . Further
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Postdoctoral position for the project Human AI Collaboration: Imaginaries, Interventions, Interfaces
the department’s teaching and supervision activities and to teach and supervise at BA and MA levels. The department has the following educations: Information Studies, Bachelor's Degree Programme in (2018
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, extract, and standardise functional information 2. Develop computational tools that integrate evolutionary and functional information using comparative genomics and deep learning approaches 3. Apply