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processing, embedded systems, machine learning, and networked communication. Each PhD position corresponds to a dedicated research topic within the consortium. All doctoral researchers will benefit from joint
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look forward to receiving your application! We are looking for a PhD student in AI and machine learning with a focus on generative machine learning methods for cyber security applications. Your work
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for a: PhD Candidate in Emotionally and Socially Aware Natural Language Processing (1.0fte) Project description Current Natural Language Processing (NLP) systems, and especially large language models
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interaction. This requires rethinking both what we measure and how we design models. The PhD candidate will: Create datasets and benchmarks that capture emotional cues, conversational norms, and cultural
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, and adapt to human interaction. This requires rethinking both what we measure and how we design models. The PhD candidate will: Create datasets and benchmarks that capture emotional cues, conversational
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support each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In the FlexMobility project we propose a holistic
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researched by another PhD candidate in the project. The developed methods could be applicable across many multi-agent coordination domains, from mobiltiy, to logistics and multi-robot systems. In this work, we
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progressed capabilities towards exploiting zero-day vulnerabilities. Frontier models show promising performance when combined with a focused knowledge base and multi-agent architectures. However, in most cases
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that the developed methods are robust, adaptable, and grounded in real-world practice. You will apply advanced techniques such as agent-based modelling, quantitative resilience assessment, and risk analysis to
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: This PhD project aims to uncover how plants perceive glycan-based Damage-Associated Molecular Patterns (DAMPs) through novel receptor kinases characterized by our group (Martin-Dacal et al., 2022; Molina et