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Field
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour
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requirements are Experience in working with large-scale spatial-temporal traffic and/or travel behavior data, e.g., loop detector, floating car data, GPS data, cellphone data. Experience with transport
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, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will have experience in one or more of these subject
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. when do we stop modelling? How do we track / score the quality of the model? What is the required level of quality over time? How can quality be brought to the required level? Can Machine Learning, Large
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Generative machine learning models have made significant progress in recent years. Typical examples include, for example, high-quality image or video generation using diffusion models (e.g
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analysis of large data sets, statistical modeling, and knowledge of at least one programming language (e. g.: R, Python and/or Julia) are required. Experience in machine learning and image recognition
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critical component analysis, and (iii) development of Automation of ML model and data selection. The applicants should have knowledge of machine learning and optical networks and willing to engage in testbed
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Your Job: As part of an interdisciplinary team, you will develop approaches for the automated and large-scale provision and integration of energy systems data and models and apply data science
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that enhance the quality and efficiency of forest management planning. The PhD student will combine remote sensing with machine learning to detect cultural remains, predict terrain accessibility, identify
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landscape, from SMEs and start-ups to OEMs and large-scale global manufacturers. For more information please visit the MTC website . Contact For further information on this PhD position please contact Dr