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techniques from statistical physics, Bayesian inference, and complex systems theory to address challenges posed by noisy and incomplete data. Depending on the results obtained in the first year, the post can
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linear ballistic accumulator models, diffusion models, biased competition models, or Bayesian models. During the employment, the candidate is expected to engage in the development of computational models
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related to gravitational wave astronomy. The primary aim will be the development of advanced approaches for computational Bayesian Inference to measure the properties of Compact Binary Coalescence signals
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propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large
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functional data ”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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(classic; Bayesian), machine learning, or other statistical approach with accompanying expertise in whatever stats package(s) is desired (SPSS; R; Stata; SAS; NumPy or PsyPy; etcetera). A strong ability to
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performance. The salary is commensurate with experience. Applications are invited from individuals who are interested in applying experimental psychology and Bayesian computational modeling to understanding
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and PhD students. Research spans a wide range. Current interests include: Bayesian statistics; modelling of structure, geometry, and shape; statistical machine learning; computational statistics; high
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learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
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at the intersection of systems neuroscience and computational modeling. Our lab is broadly interested in Bayesian inference, perception, multisensory integration, spatial navigation, sensorimotor loops, embodied