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to implement advanced computational pipelines, including machine learning, deep learning, Bayesian inference, and probabilistic mixed membership modeling for innovative research. · Contribute
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) Experience in the use of neuroimaging analysis (fMRI, MRI) to study mechanisms of brain function Previous experience of using Bayesian methods in both model development and fitting. Previous experience and
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methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon
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on hierarchical Bayesian models that allow us to integrate heterogeneous, but complementary, ecological and environmental data. Depending on the background and interest of the candidate, the work will focus on a
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in modern scientific computing -Excellent communication and collaboration skills Preferred -Experience with simulation-based inference and Bayesian methods -Familiarity with cosmological simulations
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are seeking a postdoctoral researcher to develop methods for analyzing large scale biodiversity and ecosystem function data. Our approach is based on hierarchical Bayesian models that allow us to integrate
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 2 months ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine
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. Prior exposure to experimental data from photon-counting or time-resolved detectors. Experience with Bayesian methods, uncertainty quantification, or real-time data processing. Familiarity with
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learning Deep learning model generalisation techniques Translating deep learning models into clinical settings Experience developing deep learning models for real-time image/video segmentation, object
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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from