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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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consider task success, generalisation, reliability and computational efficiency. The goal is original research for leading machine-learning, computer-vision and robotics venues. The successful candidate will
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designing and building visualization dashboards. Research experience in human-centered AI, or in the integration of AI and machine learning methods into interactive visualization and analysis systems. Strong
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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