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The Department of Computer Science at Aarhus University invites applications for a 24-month Postdoctoral Research Fellow in Explainable AI interested in interdisciplinary research between
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developing optimization-driven approaches to multimodal device tailoring. We are looking for someone with A PhD in Human-Computer Interaction or a closely related field Strong programming skills (e.g., Python
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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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knowledge exchange activities with public authorities and industry stakeholders. Contributing to teaching and supervision of students at the bachelor's, master's, and PhD levels. Qualifications We are seeking
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identifying ecological and functional connections between environmental and host-associated microbiomes. The postdoctoral researcher will work closely with a PhD student and an international network
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and different industrial outreach activities The candidate has at least the following qualifications - Applicants should hold a PhD in Computer Engineering, Computer Science, or similar - Cyber-physical
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have a PhD in synthetic chemistry or similar. The candidate is required to have a strong background in organic synthesis and transition metal catalysis. The candidate must be familiar with organometallic
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development Qualifications PhD degree in Bioinformatics, Computational Biology, Computer Science, Mathematics, Physics, or a related field Strong experience with programming in Python, R, or similar languages
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need to have a PhD in marketing, consumer behaviour, psychology, behavioural economics, environmental psychology, or another relevant field. Demonstrated experience designing, fielding and analysing
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important to integrate knowledge on processes of cognitive change and updates among individuals and in interaction with others into our data-driven computational modelling in order to understand broader