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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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-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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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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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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colleagues in the group and external partners in Denmark and abroad. You will work with the design and execution of research studies, the analysis and interpretation of data, and the dissemination of results
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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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significant contributions to fundamental machine learning research, possessing a combination of mathematical maturity and advanced engineering skills: Education: A PhD in Computer Science, Mathematics
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framework for understanding emergent deception in human-AI interaction by uniting behavioural-psychological, economic-strategic, and machine learning perspectives. The postdoc will be jointly supervised by
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experience with natural language processing and/or machine learning (e.g., through first/co-authored publications) Demonstrated interest in interdisciplinary research at the intersection of AI and law