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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing
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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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statistical analysis and modeling techniques such as Gaussian process modeling, data assimilation, and Bayesian analysis; and 4. Open-source scientific software development. Expertise in computational
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with statistical modeling (ideally Bayesian statistics) • Proficiency in Fortran, R, Python, Matlab, or ideally other common languages (e.g., C/C++) Strong computational skills Strong oral and written
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on the training strategies. In this project, we will investigate Bayesian methods to train deterministic SNNs (with deterministic activation functions) or probabilistic SNNs. Bayesian deep learning methods have
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | about 1 month 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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detection framework for tipping points. Contribute to the design of scalable and interpretable forecasting strategies for large climate simulators, integrating adaptive sampling and Bayesian techniques
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with a strong background in molecular virology, next-generation sequencing, Bayesian analysis, phylogenetic analysis, statistical genetics, and the ability to use R and/or UNIX/command line applications
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applicants for a 6-month paternity leave replacement who have a strong interest in using computational methods such as cognitive and psychophysiological modeling, (Bayesian) statistics and optimal experimental
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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