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The Department of Electrical and Computer Engineering (ECE) at Aarhus University (AU) invites applications for a tenure-track position as Assistant Professor in Electronics. We seek a talented and
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industrial energy systems that combine physics and data to become adaptive, autonomous and trustworthy? To get there, you will work at the intersection of thermal energy systems, machine learning and
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projects involve large-scale population cohorts, single-cell genomics, statistical genetics, functional genomics, machine learning, and clinical translation. We are a diverse and international team
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-Physical Energy Systems The PhD position focuses on the development of secure and trustworthy AI for resource-constrained embedded systems. The research will investigate how machine learning models can be
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problem-based learning environment, while contributing to an active research community within artificial intelligence and computer vision. The position is a permanent full time position (37 hours per week
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) invite applications from highly motivated researchers interested in an Industrial Postdoctoral position at the intersection of wireless communications, machine learning, embedded intelligence, and Internet
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work on machine learning, AI security, and real-time embedded computing, with a strong emphasis on the AI and trustworthiness side of the problem. Tasks The PhD student will contribute to research
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at Aalborg University's Technical Faculty for IT and Design. In addition, the project involves close collaboration with the Department of Architecture, Design, and Media Technology and involves one other PhD
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Assistant Professor in statistics for the development of privacy-enhancing techniques in health care
following areas: Synthetic data generation Machine learning Large health register data GDPR compliance rules Valued personal competencies include being independent and creative, having an outgoing personality
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statistics, AI and machine learning methods, including demonstrated experience in analysing multiple global change drivers, e.g. land use intensity, climate change, nitrogen deposition. Proven capability