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Infrastructure? No Offer Description The PhD candidate will work on the development of advanced statistical and machine learning methods for time series prediction, with applications mainly in the field of traffic
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such as surgery, patient or staff scheduling using, e.g., multi-objective optimization or machine learning approaches and analyzing efficiency-fairness trade offs. The research will be conducted under
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contribute 1) to the analysis methods and metrics for understanding the complex interactions between forage resource and dynamics; 2) to develop Machine Learning methods for analysing sensor data on animal
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. These methods will integrate machine learning techniques and real-time sensor data, to enhance operational efficiency, reduce costs, and ensure desired service levels, such as meeting a high percentage of demand
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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Informatics, Research Group Research-Development-Innovation is looking for a PhD-student with a doctoral grant. We invite applicants for a fully funded PhD position in machine learning, AI, and data science for
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publications and present them at well-known international conferences and workshops. Your profile M.Sc./M.Eng. Degree in telecommunication engineering, signal processing, machine learning or a closely related
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both independently and as part of an international, interdisciplinary team Assets•Experience with computer vision or deep learning (e.g. PyTorch, TensorFlow)•Familiarity with street view imagery or other
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-Development-Innovation is looking for a PhD-student with a doctoral grant. We invite applicants for a fully funded PhD position in machine learning, AI, and data science for public health within the Electronics
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, or a closely related field Strong programming skills, e.g., Python, and familiarity with machine learning and/or software engineering workflows; experience with Git and empirical evaluation Experience