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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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from the Newcastle Urban Observatory, public transport ridership data, questionnaires and travel surveys, and the DARe MATSim agent-based modelling suite, this PhD will seek to gain a deeper
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the Collaborative Research Centre for Low Carbon Living. The project uses agent based modelling (ABM) to represent consumer behaviour, social networks and their responses to non-financial incentives and barriers in