12 phd-engineer-machine-learning PhD positions at NTNU Norwegian University of Science and Technology
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Computer science » Informatics Engineering » Electrical engineering Engineering » Electronic engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Norway Application Deadline 22
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environmental change. The BEE section is a collaborative, vibrant, and growing research community including 12 Associate Professors and Professors, 23 PhD candidates, researchers, and postdocs, and 4 engineers
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designing, developing and evaluating systems and models to enhance learning through AI technology. The PhD fellow will engage with developing and evaluating models and agents, as well as, multi-agent networks
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in higher education and research, in and outside academia. This PhD fellowship is part of the newly established AI Centre for the Empowerment of Human Learning (AI LEARN) . Six national research
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consider novel design principles combining approaches in biosensors, communication systems, and machine learning. Are you motivated to take a step towards a doctorate and open up exciting career
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in higher education and research, in and outside academia. This PhD fellowship is part of the newly established AI Centre for the Empowerment of Human Learning (AI LEARN) .Six national research centers
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synthetic biology, machine learning (ML), and ultrahigh-throughput screening (microfluidics) to discover new enzymes and bioactive molecules with applications in biotechnology, medicine, and sustainability
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Academically relevant background within marine control/cybernetics, computer science, or hydrodynamics, with good skills in mathematics, programming, and machine learning. Master's degree in control engineering
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of Information Security and Communication Technology has a vacancy for 1 PhD Research Fellow in Privacy Preserving Machine Learning. The successful candidate will be offered a 3-year position. Are you motivated
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Cybernetics at NTNU is offering a fully funded PhD position in the area of learning-based control and decision-making for complex multi-agent systems. The project explores new computational frameworks