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on microscopy or time-lapse data Experience in at least one of: tracking / time-series analysis, probabilistic modelling / uncertainty, real-time or streaming pipelines Strong mathematical / statistical
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work on designing a robust, data-driven decision framework that integrates structural models, probabilistic degradation processes, and operational monitoring data. Your research will focus on identifying
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loadings. It combines experimental data, finite element simulations, and probabilistic models. Results depicted on Fig. 1 and Fig. 2 show high extrapolation and interpolation capabilities of the obtained
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), quantization and sharding, prompt optimization, reinforcement learning, Transformers/Deep-SSMs/Test-Time Regression. Experience with probabilistic machine learning, including but not limited to Gaussian
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publish solid contributions at the best machine learning conferences. STIMA is characterized by a modern view of the statistical subject, where probabilistic models are combined with computational
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interpret the architecture to local field potential data recorded in humans who have seen a vast number of images from the CoCo-database (https://cocodataset.org ); and apply and interpret the architecture
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research is applied across diverse sectors, including agriculture, manufacturing and remote sensing. Check out the team website at https://decide.ugent.be Topic As robots move from caged industrial settings
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probabilistic behavioral models for verification, performance evaluation, and optimization using model-checking techniques, ultimately bridging static system design and dynamic operational analysis. We offer
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Deviations” (TOMABOLD), funded by the Research Council of Norway. The PhD position will focus on the large deviation analysis of probabilistic models, and associated problems in PDE, with emphasis
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datasets, and large-scale statistical studies comparing different methods. The successful candidate will be jointly supervised by: Dr Edward Gillman (https://www.nottingham.ac.uk/physics/people