33 electric-machine-"https:" "https:" "https:" Postdoctoral positions at Aarhus University
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The Section for Electrical Energy Technology at the Department of Electrical and Computer Engineering (ECE), Aarhus University, is in a phase of rapid growth in both education and research
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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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-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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. You can read more about the Department of Management at: https://mgmt.au.dk/ Further information The Department of Management offers a stimulating international environment. The department conducts
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Postdoctoral position for the project Human AI Collaboration: Imaginaries, Interventions, Interfaces
full project description here, including a detailed description of SPs and WPs: https://pure.au.dk/portal/da/projects/human-ai-collaboration-imaginaries-interventions-interfaces . Information about the
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international research network and good industrial and professional collaboration. Please refer to Department of Animal and Veterinary Sciences (au.dk) for further information about the department: https
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Foundation, Independent Research Fund Denmark, etc. Sun lab: https://dandrite.au.dk/people/research-groups/sun-group DANDRITE: https://dandrite.au.dk/ PROMEMO: https://promemo.au.dk/ Department: https
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interventions and commercial marketing measures, consumer-oriented innovations, and social dynamics can be combined to support sustainable change. Please make sure to read more about the project here https
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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