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areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network
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intelligence, machine learning, big data and network analysis, computational and Bayesian methods, are encouraged to apply. Minimum Qualifications PhD in Statistics or closely related fields with documented
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generation of health data scientists. Areas of expertise include bioinformatics, computational biology, artificial intelligence, network science, Bayesian methods, spatiotemporal methods, visualization
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study examining common elements in decisions across different contexts (risk, uncertainty, time; gains, losses, and mixed domain choices). Applying Bayesian techniques to develop stochastic models
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impact-based health early warning systems. The successful candidate will join the research team of Dr. Joan Ballester Claramunt (https://www.joanballester.eu/ ) at ISGlobal within the framework
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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opportunities for collaboration with Michigan State University, and IU’s network in cognitive modeling, AI, and human–AI decision research. This postdoctoral appointment is full-time and on-campus. Job Duties 80
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-dimensional Bayesian inverse problems for image reconstruction and chemical reaction neural networks with sparsity-promoting (and edge-preserving) priors, including diffusion-based approaches. Neural solvers
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), Domus Medica, Gaustad UiO campus, Oslo. Job description The position is connected to the project “Bayesian Enhanced Tensor Factorization Embedding Structure (BETTER)”, and this PhD project specifically
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passionate about applying ML algorithms and developing AI applied research and innovation solutions using classic ML to novel transformer neural networks. We test and measure the real customer impact of each