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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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to recruit a talented researcher for a 2-year, full-time postdoc in machine learning from 1 October or soon thereafter. Your work tasks We are looking to recruit an excellent postdoctoral fellow to apply
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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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: Overcoming Inequity in Embodied Learning in Danish Vocational Education and Beyond, funded by Independent Research Fund Denmark. This is a full-time (37 hours per week), fixed-term (24 months) postdoctoral
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within this field. Your work tasks In this position you will conduct research within Computer Vision and Deep Learning, with a particular focus on the development of an AI-powered framework
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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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-in-research-and-development/software-engineering-computing-systems Further details about the Department of Electrical and Computer Engineering can be found here: https://ece.au.dk/en/about-the
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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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logical way and parametric simulation engines. Familiarity of machine learning / AI techniques and custom LLMs generation is beneficial. Knowledge of life cycle assessment (LCA) and LCA methods, carbon