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systems for discovering new concepts, experiments and ideas in physics. To accelerate this effort, we need your help! We have several fully-funded open PhD and Post-Doc positions (m/f) A list of concrete
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Position in XAI with Commonsense Knowledge for Robotics and Computer Vision 2. PhD Position in Sustainable AI for Enhancing Health Informatics (Please scroll down to read more about the project descriptions
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Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig | Leipzig, Sachsen | Germany | 5 days ago
diagnosis of dementia. This will enable specific therapy to be provided at an early stage. In the project, artificial intelligence / machine learning and new multimodal imaging methods are used to carry out
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are offering a fully funded PhD position in the field of Physics-informed Learning-based Control. This interdisciplinary research area bridges control theory, machine learning, and physics-based modeling
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Description Main supervisor: Ants Kallaste Co-supervisor: Anton Rassõlkin The research Within this thesis, the PhD candidate will learn about the control and application of additively manufactured special types
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Requirements Master’s degree (≥4 years) in Computer Science, Informatics, Engineering, Mathematics, Physics or equivalent; PhD in Computer Vision, Artificial Intelligence, Machine Learning, and Data Science, or
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approach of data-driven membrane discovery that includes material space construction and exploration, candidate selection and verification, providing data for machine learning models to optimise membrane
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within a Research Infrastructure? No Offer Description Topics In the Computer Systems Lab, we aim to hire multiple PhD students on national and international research projects in the domain of software and
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these sounds fascinating, then this PhD position is made for you! Information We invite highly motivated students with a strong background in mathematical control theory, and a keen interest in machine learning
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with e-CALLISTO instruments or Software-Defined Radios (SDRs). · Familiarity with machine learning for astrophysical data analysis. · Knowledge of solar radio data pipelines and event classification