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The project: The deployment of generative AI—particularly Large Language Models (LLMs) based on transformer architectures—in industrial settings poses several critical challenges. Ensuring reliable
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-centered and psychologically informed methods that could encourage responsible behaviours. Designing such approaches for opaque generative models, such as Large Language Models (LLMs), is critical and is
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prior experience in at least three of the following areas: Python programming Develop LLM-based tools to automate data connector generation for data ingestion. Design and implement a multi-layered storage
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conversational guides for enhancing visitors’ learning and experiences in public educational environments. The PhD student will focus on addressing the challenge of visual blindness in large language models (LLMs
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). The project aims to create a synthetic population patients using Large Language Models (LLMs). These AI-generated patient profiles will be employed to develop and test interventions designed to increase vaccine
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
aerospace environments. The objectives of the PhD are: •Extract structured engineering knowledge from unstructured maintenance data using LLMs, and represent it using ontologies and knowledge graphs •Develop
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Science, Statistics, Applied Mathematics, or a related field. • Strong background in convex analysis, statistical machine learning (reinforcement learning, LLM and generative modeling), stochastic modeling and
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applicants are welcome to contact Assistant Professor Carla F. Griggio (cfg@cs.aau.dk) regarding the scientific aspects of the position. All applications will be run through LLM/AI detectors. Applications
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on what Large Language Models have told them, and content created by archives is a part of what is used to train LLMs. LLMs have biases presenting problems in dealing with sensitive historical material, and
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data to improve sustainable mobility planning. Candidates will work with datasets from various sources, and will leverage tools like LLMs, SUMO, and MATSim for analysis, modelling, and simulation