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Field
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photographic documentation. Production of derived visual content: Generation of renders, animations, and illustrative images from 3D models for reports, scientific publications, and digital communication, with
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allows for studying language behaviour (time-course and quantity of gaze/eye-movements), neuro-physiology of language processing in the brain and neuro-imaging (https://www.ntnu.edu/langdevlab#/view
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, imaging approaches and/or bioinformatics. Postdoctoral Fellow – Banerjee Lab – 00399 Our lab is interested in how the brain accomplishes the ongoing transformation from sensation to action during natural
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view of multimodal information processing including the development of neural models for language and image using deep learning technology, with particular focus on the Portuguese language; 2
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(this overlaps with the former themes as 2D and 3D vision is often integrated with language). Related papers by the Vision-CAIR members Theme A : Aligning Latent and Image Spaces to Connect
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reconstructions of glacier variability for selected areas in Norway. This involves landscape analyses using satellite images before field mapping. The time series will be based upon studies of sediments deposited
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such as quasi-normal modes. You will lead the development of modelling codes and have primary responsibility for at least one project within the broader research program. Additionally you will contribute
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learning-based image classification approaches. The objective is to quantify landscape changes over decadal timescales, with a particular emphasis on Western Norway. Relevant transformations include
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landscape analyses using satellite images before field mapping. The time series will be based upon studies of sediments deposited in glacier-fed distal lakes analysed with ultra-high-resolution scanning
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national and international partners. The PhD project will focus on integrating advanced photogrammetric techniques applied to historical aerial imagery with modern deep learning-based image classification