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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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uncertainty, learn, and coordinate - and how these processes compare with AI. You are likely studying cognitive science, psychology, behavioral science, human-computer interaction, or another field with a
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for Statistical and computational Methods for Advanced Research to Transform biomedicine (SMARTbiomed) within the field of statistical and machine learning methods development for genetic analysis and causal
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knowledge in building ventilation systems, heat transfer, fluid dynamics, machine learning, data analysis, and coding e.g., Python. Understand sensors, actuators, data acquisition, and control systems
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The Department of Electrical and Computer Engineering (ECE) at Aarhus University (AU) invites applications for a tenure-track position as Assistant Professor in Electronics. We seek a talented and
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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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/augmented/extended (VR/AR/XR) environments to support learning of scientific concepts and practices at the university-level. The main aim of this work package is to investigate how such cutting-edge
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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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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