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og demokratisk utvikling og vere ein attraktiv og inkluderande studie- og arbeidsplass. ... (Video unable to load from YouTube. Accept cookie and refresh page to watch video, or click here to open
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of the sea, with a focus on questions related to marine protected areas About the position You will primarily work on developing and applying video-based methods for monitoring fish in protected areas and
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. Your profile You have documented expertise in marine ecology and computer vision and machine learning methods for video-based fish monitoring. You have excellent IT skills and experience in handling
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Attempto! Publications Social media Videos Podcasts Online press review Media Personalia Events Life on campus Back Events Culture, the arts and leisure time Merchandise Wellbeing Public engagement Back
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in human-computer interaction and have a strong collaboration with other research groups in Aalto and the EU. You would also be able to travel for visits or exchanges with other world-class research
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CIIMAR - Interdisciplinary Center of Marine and Environmental Research - Uporto | Portugal | 29 days ago
; Experience in processing and identification of zooplankton, macroinvertebrates and fishes; Experience in laboratory analysis; Excellent communication skills in English and knowledge of Portuguese are required
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-voltage PPU that shall power the E-Sail electron emitter, sail and associated devices. The E-Sail electron gun needs a power-processing-unit to provide the required voltages and currents. As a start
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) advances in imaging techniques that fuel a more detailed understanding of the brain, 2) tools from artificial intelligence that enable building better computer simulations of the brain. The lab will leverage
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-Sailors, but should carefully choose the ones that they apply for. Selection process Applications will be reviewed for eligibility and suitability based on the criteria listed for each position. After
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mitigate hallucinations in video-based LVLMs for autonomous driving. Objectives Design, develop, and evaluate novel method(s) to detect and localize hallucinations in LVLM outputs for autonomous driving