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-pathogen interactions and feedback, using a combination of quantitative imaging, microfluidics, statistical analysis and machine learning tools. A specific focus will be put on discovering biophysical
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principles that regulate host-pathogen interactions and feedback, using a combination of quantitative imaging, microfluidics, statistical analysis and machine learning tools. A specific focus will be put
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Christmas and 1 January; multiple courses to follow from our Teaching and Learning Centre; a complete educational program for PhD students; multiple courses on topics such as time management, handling stress
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spatial analysis and mapping tools (e.g., QGIS, ArcGIS, or spatial packages in R/Python) Interest or experience in applying AI or machine learning methods to ecological questions Personal attributes: Strong
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between Christmas and 1 January; multiple courses to follow from our Teaching and Learning Centre; a complete educational program for PhD students; multiple courses on topics such as time management
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members and over 2000+ students across its BS, MS, and PhD programs. The department currently has a strong emphasis on research in fundamental and applied computer science including AI, machine learning
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the development of headache, and how headache impacts the Norwegian economy. The project applies advanced methods in epidemiology, causal inference, genetic epidemiology, and machine learning. As a PhD candidate in
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in a previous PhD project. In addition to electromagnetic geophysics, the candidate is expected to contribute to the development of novel workflows for joint inversion of multiple data types (e.g
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, Cascais, Riga, Vilnius, Melsungen, Ciampino, Urla and Rhodes. The PhD project will involve: The use of data analytics (statistical models, machine learning, uncertainty quantification) to monitor and
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analytics (statistical models, machine learning, uncertainty quantification) to monitor and predict cycling travel conditions from various perspectives (safety, crowding, travel time, comfort, etc