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biological systems. In particular, IEMN develops advanced technologies for electrophysiological recording and next-generation brain–computer interfaces. The Lille Neuroscience & Cognition Center (LilNCog
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PhD Scholarship Opportunity - Processing intelligence for green metals using in situ X-ray characterisation and machine learning Job No.: 693787 Location: Clayton campus Employment Type: Full-time
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The Einstein Telescope is a next-generation research infrastructure currently in development, featuring a large-scale computing center whose energy demands present a unique opportunity: to develop climate-neutral operations through the integration of renewable energy sources and hybrid storage...
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incorporate methods that integrate: - Mendelian randomization and genetic instruments - Bayesian hierarchical models and Gaussian graphical models - Multi-layer data integration across tissues and omics
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, Bayesian inference, model calibration, and Markov Chain Monte Carlo methods, uncertainty quantification, statistical modelling, and Gaussian processes, machine learning for time series, sequence-to-sequence
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identified by, a set of parameters. Such methods are a popular choice in quantum dynamics, where the wavefunctions of many-body systems are approximated by multiple Gaussians. The methodology of interest
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of the processes governing these interactions remain poorly understood and are inadequately represented in current climate and Earth system models, limiting our ability to accurately simulate and predict future
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of settings where only parts of a system are extreme. Special attention will be given to parametric families like the Hüsler–Reiss distribution, which lead to an extremal analogue of Gaussian structural causal
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interactions to neutron-rich systems. The central aim of this project is to develop a Gaussian Process emulator for computationally expensive Relativistic Hartree-Bogoliubov and Quasiparticle Random Phase
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development. The next step is to incorporate the evaluation of trajectory parameters (Track Fit) into this process. A promising approach involves regression using Transformers, which have shown excellent