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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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LVLMs for autonomous driving. Objectives Design, develop, and evaluate novel method(s) to detect and localize hallucinations in LVLM outputs for autonomous driving tasks Investigate and propose mitigation
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malicious behaviors implemented in Smali code. Adversarial attack development: you will design and implement adversarial attacks by manipulating localized malicious payloads against malware detection models
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LLM-based vulnerability detection systems. Robustness evaluation framework: you will develop comprehensive assessment methodologies to quantify the weaknesses and limitations of LLMs in software
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this context, the role of subjective and objective measures, and their integration, is crucial for creating robust and interpretable AI models. Today, the complexity of multi-dimensional data and the need
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research focused on biomedical image computing. Our work involves developing state-of-the-art methods for image segmentation, detection, classification, predictive modelling, and image enhancement. We aim
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security. This project investigates how blockchain analytics and data engineering can enhance the detection of illicit activities across these decentralised and increasingly complex networks. By deploying
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other sources. This enables the sensor to capture fine details about an object's position, velocity, and movement direction, which can be invaluable for applications such as medical imaging, autonomous
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The University supports researchers to acquire funding from national, European and private sources But wait, there's more! Complete picture of the perks we offer Discover our Partnership Programme How to apply