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problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct world-leading fundamental and applied research within communication, networks, control
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for spinal surgery. The Candidates for this stipend should have a background in software engineering or similar and have substantial experience with machine learning. All cases involve various degrees of image
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algorithmic solution development. The group focuses particularly on automated decision-making in autonomous cyber-physical systems, combining mathematical optimization, machine learning, and decision theory
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. Mathematical skills: Competence in mathematical modeling of dynamic systems and probabilistic frameworks. Experience with machine learning or AI methods for localization or perception (e.g. learning-based SLAM
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on the development of AI models for analysis of cardiac CT scans, with the aim to explore how machine learning models can quantify cardiovascular disease and predict future events from CT scans. The project will
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% of all employees are internationals. In total, it has more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its unique
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team. Significant software development experience in several key languages, e.g., Rust, C++, or Python (not MATLAB), algorithms, and machine learning is necessary as well as excellent communication
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properties through computer simulations. The project also includes validation of materials in the lab and translating the findings into practical recommendations for use in real power-electronics environments
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statistics This PhD project falls under the collaboration between Research Thrust RT2 Physics-based models, and Research Thrust RT3 on representation, compression, learning, and inference. For long-distance
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design and laboratory experimentation, you will first explore a broad range of sodium-oxide glass compositions using advanced computer models to predict how well ions can move through them. Based