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based on Artificial Intelligence, Machine Learning and Data Science for the modelling, analysis and interpretation of complex biomedical systems. Research activities will include the design and validation
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initiation latency, movement speed, gait characteristics, postural control, motor variability, and other behavioural descriptors. ESSENTIAL REQUIREMENT PhD in machine learning, artificial intelligence
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proficiency in coding, knowledge of GIS environments and point cloud processing, strong interest in heritage scenarios as well as a collaborative attitude for interdisciplinary work between computer scientists
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management, analytics, machine learning, and artificial intelligence. Its objective is to contribute to the advancement of scientific knowledge in the field of data-centric systems by addressing the challenges
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between Numerical Analysis and Machine Learning, with a focus on physics-informed machine learning. The goal is to design learning strategies that incorporate the structure of the physical laws governing
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. – knowledge of computer vision; knowledge of deep learning architectures; – Knowledge of C++, Python, Matlab; – Analog/digital circuits IC design capability; – Testing of electronic devices and systems; FPGA
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of qualifications. Interview on: a) discussion on submitted qualifications and publications b) assessment of skills and knowledge in the field of: - Artificial Intelligence, Machine Learning and generative models
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, Physics, Electrical Engineering, Communication Engineering, or equivalents; – Knowledge in artificial intelligence, statistical and machine learning, complex systems, agent-based modeling and simulation
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The position requires developing and implementing machine learning models
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regression, survival analysis, classification, and machine learning techniques. Familiarity with bioinformatics workflows, reproducible data analysis, and high-performance computing (HPC) environments