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applications from researchers specializing in probabilistic and neuro-symbolic AI. Areas of interest include, but are not limited to: • Probabilistic machine learning • Deep probabilistic graphical models
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/PhD) You can learn more about the recruitment process here . Applications received after the deadline will not be considered. All interested candidates irrespective of age, gender, disability, race
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The Department of Public Health at Faculty of Health at Aarhus University invites applications for a position as Associate Professor in the field of statistical and machine learning methods
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include data science management and development of novel and executing existing computational methods including machine learning and deep learning methods to integrate genomics, transcriptomics and
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modelling, advanced machine learning tools, etc. We welcome applicants with a strong academic background within engineering or applied science, whose expertise supports the development of resilient and
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. To achieve this, the Center will develop and deliver research-based education for the future workforce – spanning bachelor, master, PhD, and life-long learning. The Center is based upon grant funding of DKK
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Assistant Professor in Electrical Energy Technology, Department of Electrical and Computer Engine...
. Supervising student projects and theses, particularly within converter technologies, electrolysis integration, and power system stability. Successful candidates should have a PhD in electrical engineering or a
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this, the Center will develop and deliver research-based education for the future workforce – spanning bachelor, master, PhD, and life-long learning. The Center is based upon grant funding of DKK 123 million from
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externalities of transport. The division is interdisciplinary with scholars originating from transportation engineering, economics, psychology, computer science, social data science, machine learning, mathematics
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often related to domesticated species and humans, but increasingly also on other organisms. Our focus areas include quantitative genetics, deep learning, machine learning, population genetics, integrative