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of this unusual Brain-Computer Interface. More information: (7) BRAINET: Overview | LinkedIn The doctoral candidate at Tampere University will develop tools and methods to assess the effects of the non-invasive
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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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experimental psychology and cognitive science to augment human intelligence with AI. Your experience and ambitions eligible for PhD study at Aalto University (https://into.aalto.fi/display/endoctoralsci/How
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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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must be ready to travel Applicants must be eligible to enroll on a PhD program at TU Dresden (see https://tu-dresden.de/ing/maschinenwesen/postgraduales/promotion?set_la… ) LanguagesENGLISHLevelExcellent
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CIIMAR - Interdisciplinary Center of Marine and Environmental Research - Uporto | Portugal | about 2 months 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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. This PhD targets principled ways to detect, analyze, and mitigate hallucinations in video-based LVLMs for autonomous driving. Objectives Design, develop, and evaluate novel method(s) to detect and localize
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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