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and cooperation. You have a strong interest in machine learning/artificial intelligence. You have experience with deep learning. You have experience with reinforcement learning (preferred). You have
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for using AI to develop social engineering attempts. This project combines human subject research of learning and decision making, Human-Computer Interaction, and the advancement in AI methods
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development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Experience with machine learning and neural networks Basic
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to correlate polymerisation kinetics, macromolecular architecture, morphological evolution and drug encapsulation mechanisms. Beyond experimental work, the project will integrate machine learning approaches
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project topics can be found at https://indibrain.eu/recruitment . OPEN POSITION: Project 5: Modeling Individual Brain Differences using Deep Learning & Vision Neuroscience: This project sits
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). Desirable assets are: • Machine learning, deep-learning, artificial intelligence, advanced statistical inference; • A solid record of research activities, including relevant publications in international peer
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and cooperation. You have a strong interest in machine learning/artificial intelligence. You have a strong interest in graph-based learning (e.g., graph neural networks). You have experience with deep
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and research interests, the project may focus on one or more topics such as cyber risk assessment, human–machine interaction, cyber risk communication, cybersecurity training, or organizational and
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
analysis, image analysis, statistical, and machine learning methods, choosing the approach that best fits the scientific question. Investigate systematically what information is contained in imaging data
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be advantageous: Experience with scientific expeditions Experience in computer-aided analysis of biological sequence datasets (e.g., with R, Python and Bash/Linux environments) Basic understanding