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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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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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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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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
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areas will be considered when selecting candidates: Machine Learning, Neural Networks, Numerical solutions of Partial Differential Equations and Stochastic Differential Equations, Numerical Optimization
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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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, 4) Model interpretability. Experience with other deep learning methods, such as Convolutional or Bayesian Neural Networks, Simulation-Based Inference (SBI), Normalizing Flows, or Diffusion Models, is
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related to Riemann-Steltjes optimal control to combine PMP with Bayesian Optimisation, allowing for data-efficient learning. You will then implement and validate the new method on simulated fermentations
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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