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Field
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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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immune cells in cell culture and murine models of inflammation and cancer Collecting, analyzing and annotating MRI data Combining MRI and mass spectrometric imaging data in data base for quantitative MRI
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description Current Natural Language Processing (NLP) systems, and especially large language models (LLMs), are interacting with human emotions more readily than earlier AI systems, but we still lack frameworks
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) Project description Current Natural Language Processing (NLP) systems, and especially large language models (LLMs), are interacting with human emotions more readily than earlier AI systems, but we still
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children with autism and eating behavior challenges. This research explores how large language models (LLMs), interactive robotics, and multimodal technologies can support creative expression and
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, machine learning and behavioral modeling methodologies. In the FlexMobility project we propose a holistic approach to design a public transport network that includes both traditional fixed lines and
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model-based control tailored to systems like NudgeFlow. The framework decomposes the global control problem into smaller local problems for each zone or room, enabling independent optimization with local
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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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university degree (diploma, master's degree) in transport or traffic flow or related study programs. Solid knowledge of at least one programming language, preferably Python, experience in model-based
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-based modelling, quantitative resilience assessment, and risk analysis to simulate and optimise resilience strategies. The framework will be tested and refined through pilot studies in collaboration with