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FOR DRAWING UP OF PREDOCTORAL CONTRACTS FOR THE TRAINING OF DOCTORAL STUDENTS FUNDED BY THE UPV'S RESEARCH STRUCTURES – SUBPROGRAMME 2 (PAID-01-22) 119865 Development of machine-learning and graph-based models
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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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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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. The objective of the research is to use machine learning methods to find models of ship trajectories and traffic patterns that can be used to detect anomalies and predict into the future. The basis for this is
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Deadline 28 Feb 2026 - 23:00 (UTC) Type of Contract To be defined Job Status Full-time Hours Per Week To be defined Is the job funded through the EU Research Framework Programme? Not funded by a EU programme
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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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, 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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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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, 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