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for objective, data-driven rehabilitation through predictive analytics. This interdisciplinary PhD project, conducted in collaboration with the Department of Occupational Therapy and Physiotherapy at Sahlgrenska
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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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data handling Strong self-initiative and willingness to learn and to quickly gain an understanding of new subject areas Joy in working independently, as well as within a team Strong problem-solving
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such as the NEPS. Potential research areas include (but are not limited to): Item response modeling of achievement tests Analysis of process data (e.g., response times) to enhance competence measurements
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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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assistant is a fixed-term scientific position of 6 months. If you have hands-on experience with analytical chemistry and laboratory techniques and experience with troubleshooting, optimization of laboratory
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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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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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formation under conditions as natural as possible along the gradient. We will measure 13CO2 fluxes in the field and combine these data with the incorporation of the 13C tracer into different SOM fractions