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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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The Section for Software Engineering and Computing Systems, at the Department of Electrical and Computer Engineering (ECE), invites applicants for a one-year postdoctoral position within the area of
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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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. 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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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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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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populations. Apply Artificial Intelligence (AI) methods including deep learning (DL) models and supervised and unsupervised machine learning (ML) methods for integration and for Genome-2-Phenome (G2P) and risk
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developing optimization-driven approaches to multimodal device tailoring. We are looking for someone with A PhD in Human-Computer Interaction or a closely related field Strong programming skills (e.g., Python