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Recognition, Image Processing, Natural Language Processing, Virtual Reality and Game, Robotics, Software Engineering, Programming Languages, Multimedia, Computer Graphics, and Visualization, etc. Job
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using conventional microbiology, microscopy, and molecular methods; - Correlate microbial activity with electrophysiological sensor responses; - Support prototype validation at partner facilities
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the collaborative MURIDAE Cluster of the MRC National Mouse Genetics Network (NMGN, https://nmgn.mrc.ukri.org/clusters/muridae/ ). The MURIDAE project aims to elucidate how recently identified schizophrenia risk gene
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verification of the recognition of the degrees obtained abroad will be fulfilled for the purpose of contracting, in case of approval of the candidatures. Further information at https://www.dges.gov.pt/en/pagina
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. Demonstrated experience applying statistical modeling and/or machine learning methods to research problems, e.g., text mining, natural language processing, image segmentation, voice recognition, etc. Knowledge
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for EcoCARE Labs, including the definition of image and sound collection scripts, informed consent procedures, archiving criteria, and metadata. Participation in the collection and editing of short ethnographic
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] * Background of the recruitment and description of the project Understanding the meaning of videos and images is a central research topic in the fields of computer vision, pattern recognition, machine learning
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stress recognition and its architecture. Expected practical results: Prototype of a stress recognition system and its demonstration. Typical stressful situations for teachers recreated in virtual reality
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the means and practices to ensure that all donors receive appropriate, consistent recognition and accounting of the impact of their gifts. Enhances donor prospecting and stewardship for major gift
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diagnostics, as well as general computer vision applications including autonomously guided vehicles (particularly self-driving cars), image-based localization, structure-from-motion, and object recognition