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linearization with limited or imperfect models. Learning-enabled control dynamics Embedding optimization and learning algorithms (e.g., SGD, Bayesian updates) into control design and analysis. Attack-tolerant
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optimization-based updates (e.g., stochastic gradient methods and Bayesian learning), Probabilistic performance guarantees, leveraging tools from stochastic systems, RKHS-based learning, and Bayesian inference
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Posting Summary Logo Posting Number RTF00070PO26 USC Market Title Associate Scientist Link to USC Market Title https://uscjobs.sc.edu/titles/156374 Business Title (Internal Title) Associate
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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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modelling: -Weighted PINNs, -Bayesian PINNs, -Stochastic PINNs, -Ensemble PINNs, -Domain-decomposition PINNs. Selected approaches will be tested within a dedicated data-assimilation framework
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expertise/interest in Bayesian methods for addressing measurement error. Ideally PhD within the last 5 years. Advanced level experience with R, desired knowledge of Nimble, Overleaf. Excellent communication
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is not recommended to select Autofill with Resume when applying if using a resume or CV which exceeds one (1) page. Prior to submitting your application, please review and update (if necessary
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investigating how data-efficient and resource-efficient techniques, such as data attribution, data selection/reweighting, data valuation, data curation, Bayesian optimization, active learning, can be applied in
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Appl Deadline: none (posted 2025/10/30 04:00 AM UnitedKingdomTime, updated 2025/10/24) Position Description: Apply Position Description Professor of Practice in Transportation Engineering The School
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mechanistic accounts, the Bayesian predictive coding framework has gained increasing prominence. According to this framework, perception of proprioceptive input and voluntary movement is shaped by top-down