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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
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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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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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factors. The LCSB recruits talented scientists from various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently
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to the inexistence or insufficiency of candidates to continue the contest. IV. Selection methods and criteria IV.1. Selection Methods: Assessment of scientific background and CV [ABC (90%)] and Interview [I (10%)]. IV
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science, to deliver molecular mechanics force fields that bridge the gap to quantum mechanical accuracy for biological modelling and computer-aided drug discovery. They will develop electrostatic embedding
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to the inexistence or insufficiency of candidates to continue the contest. IV. Selection methods and criteria IV.1. Selection Methods: Assessment of scientific background and CV [ABC (90%)] and Interview [I (10%)]. IV
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14 Nov 2025 Job Information Organisation/Company University of Beira Interior Research Field Engineering » Computer engineering Engineering » Mechanical engineering Engineering » Electrical
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the positions cannot be totally occupied, due to the inexistence or insufficiency of candidates to continue the contest. IV. Selection methods and criteria IV.1. Selection Methods: Assessment of scientific
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the area of program evaluation and survey research, including identifying funding sources and RFPs; conducting literature reviews; developing research question; designing research methods, data analysis plan