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- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
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benefits in the State Pension Plan Opportunity for physical activities within working hours Salary PhD Research Fellow (code 1017): NOK 550 800 a year. Further promotion will be based on time served in
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research project within the framework of this announcement, leading up to a completed dissertation Learn relevant methods and theories to be used in the research Complete the obligatory training component of
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, for instance, those based on processes of storytelling, creative writing, cognitive science, narrative theory, and/or theories of creativity. The successful candidate will conduct collaborative as
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writing, cognitive science, narrative theory, and/or theories of creativity. The successful candidate will conduct collaborative as well as their individual research within the Center for Digital
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technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data
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, or experiences and understandings amongst different groups of citizens. A variety of analytical perspectives and qualitative methods are relevant, including public connection research, folk theory analysis
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groups of citizens. A variety of analytical perspectives and qualitative methods are relevant, including public connection research, folk theory analysis, critical algorithm studies, ethnography and
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, collaborating across disciplines to tackle fundamental challenges through innovative methods, theory and critical analysis. The fellowship period is 3 years. Starting date as soon as possible and upon individual
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. Integreat develops theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data, laying the foundations of next generation machine learning. We do this by combining
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the Research Council of Norway. In this project, we will use advanced time-lapse imaging, numerical simulations, and reactive mixing theories to better understand and predict the role of fluid mixing as a driver