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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 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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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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-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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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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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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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
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. Qualifications Applicants at Postdoctoral Researcher level should hold a PhD in AI enabled learning, educational technology, information systems, computer supported learning, social entrepreneurship, innovation