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theories from tractable models (probabilistic circuits) and Bayesian statistics to tackle the reliability of machine learning models, touching topics such as uncertainty quantification in large-scale models
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one or more of these subjects are valued: machine learning, automatic control, system identification, optimization, signal processing, filtering and smoothing, probabilistic modelling, dynamical systems
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integrated multimodal models on synchronized speech, body motion, and facial data. We are seeking a postdoctoral researcher with a strong background in probabilistic models, ideally applied to speech or human
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the world, but extinction because of climate change and human pressures. Our project will use cutting-edge imaging and AI-driven analysis to reconstruct their life histories and forecast future reef
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widely in e.g. safety-critical (embedded) systems, data analysis, weather forecasting, physics, and engineering. Verifying that such programs are correct is challenging because of rounding errors due
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measurement and control platform for optimal island operation of Chalmers’ wind-battery system. Machine learning-based forecasting tools for renewable production using limited local measurement data