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experience in AI teaching We understand AI very broadly – and adequate experience would include most topics in modern statistics and topics like Bayesian Machine Learning and Simulation Based Inference (a past
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Details Panel (longitudinal) data enables learning the dynamics and relations of (groups of) units, strengthening the inference on both cross-sectional and dynamic parameters. The dominant approach
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, transcriptional recording (Record-seq), and related technologies. Develop and apply statistical methods for demultiplexing, normalization/QC, effect-size estimation, biological inference, and predictive modeling
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Bayesian Index Tracking: optimisation by sampling School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Kostas Triantafyllopoulos, Dr Dimitrios Roxanas Application
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experience would include most topics in modern statistics and topics like Bayesian Machine Learning and Simulation Based Inference (a past research focus on neural network architectures is not a prerequisite
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. Compare advanced deep learning–based methods with probabilistic approaches. Collaborate with researchers at Chalmers, the University of Gothenburg, and international experts in Bayesian inference and
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. Collaborate with researchers at Chalmers, the University of Gothenburg, and international experts in Bayesian inference and optimal control. Present your results at international conferences and publish in
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 22 days ago
specifically, we use simulation-based inference (SBI) [1], a Bayesian approach that leverages deep generative models, such as conditional normalizing flows and score-diffusion models, to approximate
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evidence synthesis, including Bayesian inference as well as effective interpersonal skills. You will work alongside an interdisciplinary team to deliver the research aims. In addition, the postholder will be
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Scalable Inference: Develop new algorithms for scalable uncertainty quantification (UQ) and Bayesian inference and apply them to challenging simulation problems. The goal is to produce robust, validated