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Field
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stimulating, interdisciplinary environment. develop and validate machine learning models to extract digital biomarkers for atypical parkinsonism from real-world wearable sensor data. interpret findings in close
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experience with CAD/FEM software Experience in one or more of the following fields is a plus: simulation frameworks (e.g. SOFA, NVIDIA IsaacLab), ROS1/2, machine learning, computer vision Excellent
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industries: in-car systems, medical devices, phones, sensor networks, condition monitoring systems, high-performance compute, and high-frequency trading. This CDT develops researchers with expertise across
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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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, Machine Learning, Artificial Intelligence, Computational Linguistics, or a related field) Strong skills in machine learning and deep learning Experience with modern NLP methods, including transformer models
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, target recognition and shape estimation, data association, as well as intention prediction, beyond the state of the art. In order to support machine learning, the project will make use of historical radar
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modelling. Some experience with programming in R and/or Python. Exposure to climate or weather data, forecasting systems, or geospatial tools. Understanding of or curiosity about machine learning, AI
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Computational Imaging Research Lab (CIR), Department of Biomedical Imaging and Image-guided Therapy | Austria | about 1 month ago
, startups and patient partners. Your Mission as PhD is to develop novel machine-learning methods to (1) detect small, clinically meaningful changes in joints, (2) enable robust longitudinal monitoring and
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manufacturing, health and transportion: Interperability of systems Data Management Big data analytics AI and Machine Learning Experience in the organization of international events such as workshops and
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to improve detection capabilities, target recognition and shape estimation, data association, as well as intention prediction, beyond the state of the art. In order to support machine learning