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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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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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) 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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(mammalian cell cultures) and whole tissue level (preclinical cancer models). Main Tasks and responsibilities: The PhD candidate researcher will focus primarily on laboratory-based experimental work in the
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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
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, an agent-based physiological 3D model of mitochondria based on the seminal work of Prof. Skupin (https://www.nature.com/articles/s41598-019-54159-1 ) will be extended and embedded into a continuous
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Universities (CAO NU)); 8% holiday pay and 8.3% year-end bonus; a pension scheme, partially paid parental leave and flexible terms of employment based on the CAO NU. In addition to the terms of employment
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Cybernetics at NTNU is offering a fully funded PhD position in the area of learning-based control and decision-making for complex multi-agent systems. The project explores new computational frameworks
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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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-based control and decision-making for complex multi-agent systems. The project explores new computational frameworks that combine principled reasoning with the efficiency of modern machine learning