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dynamics simulations is highly desirable. Basic knowledge of machine learning is considered an advantage but is not mandatory. LanguagesENGLISHLevelExcellent Additional Information Work Location(s) Number
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through applied research programmes. Faculty in the ICT Cluster undertake funded industry-relevant research, teach courses in Computer Science, Computer Engineering, Information Security and Software
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skills, including proficiency in statistics, scientific programming, and/or modelling. We especially welcome candidates interested in applying AI and machine learning to analyse heritage datasets
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statistics, scientific programming, and/or modelling. We especially welcome candidates interested in applying AI and machine learning to analyse heritage datasets. What we offer: • A stimulating
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topics such as statistics, high performance programming, machine learning and using data to constrain cosmological models. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs
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Digital. The research focuses on advanced signal analysis and machine learning methods that enable robust operation and service continuity in future wireless networks under challenging radio conditions. As
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perform 3D single-particle tracking and establish pipelines to characterise the particle motion using a combination of established tracking algorithms and machine-learning-based approaches. Additionally
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position in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic
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Shifting the paradigm: machine-assisted scholarly digital editing Digital Humanities Institute PhD Research Project Self Funded Dr Isabella Magni Application Deadline: Applications accepted all year
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candidate will hold a PhD in geosciences, applied machine learning, data assimilation, or applied mathematics. The selection will be based on the following scientific and technical criteria: Experience in