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in engineering applications. This includes working with hybrid approaches that combine advanced damage models with machine learning-enhanced numerical simulations, and exploring how these approaches
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: Safeguarding Users’ Cognitive Autonomy in Human-AI Interaction Cognitive autonomy, our fundamental ability to think independently, is under threat from growing reliance on artificial intelligence (AI) technology
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, is under threat from growing reliance on artificial intelligence (AI) technology. While designed to assist, AI recommendations may inadvertently impede cognitive reasoning and steer decision-making
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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
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-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus on implementing
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within battery state estimation, with a particular focus on emerging sodium-ion battery technology. You will be involved in both independent and collaborative research activities in close interaction with
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data or large data volumes in all information systems. We contribute methods and algorithms for machine learning, and data mining, including XAI, as well as for data access and query processing. Aarhus
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statistics, AI and machine learning methods, including demonstrated experience in analysing multiple global change drivers, e.g. land use intensity, climate change, nitrogen deposition. Proven capability
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should have the following qualifications: Ph. D. degree in data science, electrical engineering, computer engineering, computer science, mathematical engineering, or similar. Proven track record in