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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
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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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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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of the project. Apply established techniques and develop new methods inspired by Bayesian methods and statistical physics methodology for understanding the emergence of structure within cortical organoids and
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surveillance) sensors can also be seen as temporal events. While data from current sensors can be manually converted into events for fast processing, it is also possible to develop hybrid structures where some
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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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orthogonalization (differentiating memory representations to prevent interference) and abstraction (extracting shared structure across tasks to facilitate generalization). These models will be validated through
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human language evolve? What are the social, cognitive and environmental pressures that drive the emergence of language and shape its structure? As a postdoctoral researcher, you will explore
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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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broader compensation structure. We are committed to offering competitive and flexible compensation packages to attract and retain top talent. Benefits Rutgers offers a comprehensive benefits package to