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Perception Lab. Your competencies You have a strong background in computer vision and artificial intelligence, documented by a relevant PhD degree in, for example, computer science, machine learning, computer
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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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Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position
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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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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
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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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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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) 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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: Conceptualisation and synthesis of data integration and visualisation workflows. Data management and the development of knowledge graphs Development and application of AI and machine learning methods and pipelines