42 data-visualization-analysis "https:" PhD positions at Aalborg University in Denmark
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patterns representing health incidents and the development of privacy-preserving methods for visualizing health data. What you will gain: Strong expertise in statistical and computational methods for privacy
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deep learning, computer vision, medical image analysis or unsupervised learning is an advantage. English language skills, both written and spoken Qualification requirements PhD stipends are allocated
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associated with interpretation of measurements from blood and, in particular, measurements from a blood gas analyser. Often, blood samples can be incorrectly assessed due to gas contamination, delayed analysis
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analysis. You master programming in for example Python, MATLAB, or a similar platform and you are motivated to further develop your skills in scientific computing and hyperspectral data processing. You have
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solution for the future of maritime decarbonisation. Your work tasks The research assistant will be involved in high-fidelity numerical modelling and a comprehensive analysis campaign of flexible power
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research questions, and be able to think critically and develop your own scientific ideas. Previous experience with statistical analysis, programming (e.g., R or Python), machine learning, or genomic data
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-driven modelling. Experience with numerical modelling, simulation, optimization, control, or engineering-data analysis. Good programming skills in Python, MATLAB/Simulink, or a comparable scientific
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ICT, Learning and Organizational Change or the continuing education programme in IT and Learning. Information about the academic content of the programmes is available here: https://www.aau.dk
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dissemination is expected to focus on leading Human-Computer Interaction venues. For further information about the project, see: https://dff.dk/en/our-funded-projects/meet-the-researchers/research-leaders
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onwards. Through a combination of history of emotions, educational history, historical ethnography, and policy analysis, the project explores how students' discomfort with, resistance to, and disengagement