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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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Join a research environment where Artificial Intelligence and Machine Learning moves beyond theory into clinical impacts. At the Faculty of Engineering and Science, this postdoctoral position offers
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. The work combines physics-based thermal design and process-level system simulation with high-fidelity computational fluid dynamics and fast reduced-order and machine-learning models, so that the final design
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others. Essential: Strong data analysis and machine learning skills and experience with PyTorch (or equivalent frameworks). Hands-on experience with data representation and embeddings, ideally applied
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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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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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designing and building visualization dashboards. Research experience in human-centered AI, or in the integration of AI and machine learning methods into interactive visualization and analysis systems. Strong
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) invite applications from highly motivated researchers interested in an Industrial Postdoctoral position at the intersection of wireless communications, machine learning, embedded intelligence, and Internet
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studying how wireless sensing and AI interact in real systems. The work will be carried out in close collaboration with researchers in wireless communications, sensing, machine learning, and robotics, with
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studying how wireless sensing and AI interact in real systems. The work will be carried out in close collaboration with researchers in wireless communications, sensing, machine learning, and robotics, with