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The Analytical Chemistry Group invites applications for a 4-year assistant professorship in machine learning–based non-target screening of contaminants, CECs and other complex chemical mixtures
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or related fields (with the degree obtained no longer than four years before the application deadline. Exceptions can be made with documented career breaks eg parental leave, illness, mandatory military/civil
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technologies Development and utilization of high throughput methods for characterizing and quantifying the physicochemical behavior of food macromolecules in complex matrices. Modelling and the use of machine
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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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technologies Research Field Computer science » Informatics Computer science » Cybernetics Computer science » Other Engineering » Computer engineering Engineering » Electronic engineering Engineering » Other
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architectural firm. The candidate is expected to publish in leading Human-Computer Interaction venues. Your competencies You hold a master’s degree in human-computer interaction, computer science, interaction
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Researcher level should hold a PhD in AI enabled learning, educational technology, information systems, computer supported learning, social entrepreneurship, innovation management, or a closely related field
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of bioacoustic signals. Extensive experience with programming (Matlab, R, Python) including GPU programming is required, and familiarity with edge-based machine learning (particularly sound event detection), open
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of Civil and Architectural Engineering Theme 9: Building the resilient, flexible, multi-energy microgrids of tomorrow hosted by the Department of Electrical and Computer Engineering Theme 10: Advanced
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Two DTU Tenure Track Assistant Professors in Autonomous Modelling and in Robotic Synthesis of Ene...
and robotics. As the successful candidate, you will develop innovative research programs spanning atomistic and mesoscopic materials simulations, machine learning, foundation and surrogate models