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Ghorbani, S., Wloka, C., Etemad, A., Brubaker, M. A., & Troje, N. F. (2020). Probabilistic character motion synthesis using a hierarchical deep latent variable model. Computer Graphics Forum, 39(8
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Learning in a broad sense. Particular areas of interest include, but are not limited to, development and analysis of machine learning models for scientific computing, theory and algorithms for sampling
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, Taoyuan 320317, Taiwan [map ] Subject Areas: Atmospheric Sciences / Remote sensing Meteorology / Typhoon dynamics and numerical modeling Data Mining & Machine Learning / Artificial Intelligence (AI
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upcoming areas off the beaten paths. Our three main areas of research are machine learning, distributed systems, and theory of networks. Within these three areas, we are currently working on several projects
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each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In FlexMobility we propose a holistic approach to design
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, to produce synthetic data. Embedding models for wireless channel representation, e.g., transformer-based, leveraging reconstruction or contrastive learning paradigms. Main research field: Telecommunications
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proficiency in Python and experience with machine learning frameworks such as PyTorch or TensorFlow; Experience with MDE and Object Detection models; Experience in depth data collection and curation, including
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(completed or near completion) in Computer Science, Computer Vision, NLP, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative modelling (diffusion
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the strategy to be applied for training a learning model to predict wind and precipitation conditions based on a dataset combining numerical weather prediction models and in situ observations; Collect
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areas of data science such as machine learning, predictive modeling, decision-making under uncertainty, artificial intelligence, or computational statistics. This position is expected to strengthen