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safety-critical domain, reducing hallucinations and improving robustness and trustworthiness are essential. This PhD targets principled ways to detect, analyze, and mitigate hallucinations in video-based
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. Robustness evaluation framework: you will develop comprehensive assessment methodologies to quantify the weaknesses and limitations of malware detection models. Countermeasures: you will develop targeted
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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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, materials characterization and device testing. Extensive experience with clean room processes and testing of resonators is required. •Enjoy working in an international team. Solid track of communication
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evaluation platform that tracks and ranks state-of-the-art LLMs for vulnerability detection, conducting rigorous testing to ensure accurate assessment of model robustness against adversarial attacks. Is Your
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, DRX, XPS, etc.). Experience in Atomic Layer Deposition, mechanical, electrical and/or thermal characterizations of materials, is a plus. High-level scientific and technological track record related
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of materials, is a plus. High-level scientific and technological track record related to material chemistry and/or electroplating is a plus. Strong communication and interpersonal skills; The candidate should be