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description The position is connected to the project “Bayesian Enhanced Tensor Factorization Embedding Structure (BETTER)”, and this PhD project specifically aims at developing a unified, scalable, and
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, to quantum topology, mathematical physics, complex analysis, and dynamical systems. The statistics group research areas include biostatistics, Bayesian methods, environmental and ecological statistics
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) incorporation of expert knowledge in model building through Bayesian prior elicitation, and 3) develop new methods for identification of conflicts in different parts of complex models. BioM is an
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getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied
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intelligence, machine learning, big data and network analysis, computational and Bayesian methods, are encouraged to apply. Minimum Qualifications PhD in Statistics or closely related fields with documented
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related to Riemann-Steltjes optimal control to combine PMP with Bayesian Optimisation, allowing for data-efficient learning. You will then implement and validate the new method on simulated fermentations
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. Proficiency in Python, MATLAB, or R. Strong quantitative and analytic skills. Preferred Qualifications Experience with evidence-accumulation models (DDM, sequential sampling, Bayesian models). Experience with
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equalizer (DFE) and a channel decoder based on PGMs and BP. The proposed research project aims to explore when and how combinedGNNs and PGMs can improve Bayesian receiver design and beamforming for multiuser
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used will the information-theoretic Bayesian minimum message length (MML) principle. Student cohort PhD, possibly Master’s (Minor Thesis) or Honours URLs/references Chen, Li and Gao, Jiti and Vahid
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with