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Field
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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
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and implantable sensors o Microfluidic-based electrochemical platforms o Data-driven sensor signal processing Fluent English proficiency (B2/C1 level or higher). Strong analytical, problem-solving
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will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show challenging properties of uncertainty, irregularity and
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to investigate the health and welfare of people with idiopathic scoliosis throughout the entire life course, including the transition from youth to adulthood. Registry data for the entire population of Norway from
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and complete a PhD dissertation Participate in scientific conferences and events to present research findings and engage with the academic community For more information about the project, please get in
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related discipline. You will have strong experience in one or more of the following areas: electrified powertrains, marine robotics systems, automation, or predictive maintenance using data analytics
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geospatial data on fisheries activities near OWFs to evaluate the distributional impacts of OWFs across different fleet segments; conducting fieldwork and surveys in collaboration with ‘NO REGRETS’ partners
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proper customization, fine-tuning, extension to include context- or time-specific information and above all how to make all the process as much as automated as possible to support the automatic analytics
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of the following topics: Strong emphasis on telecommunications and networks theoretical background Knowledge of 3GPP standards up to 5G at the system level Data Analytics and Management Problem solver Knowledge
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consolidated data model and advanced analytics to facilitate strategic decisions with relevant stakeholders. The candidate will be involved in various (inter-)national initiatives and engaged with different