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geoscientific process models, as demonstrated by presentations, publications and/or repositories Expertise in applying Bayesian statistical methods, machine learning methods, or related statistical inference
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Computational Systems Biology group and has extensive expertise in Bayesian inference for biological systems. Project description Ordinary differential equation (ODE) models provide interpretable descriptions
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in large pre-trained models (vision-language models), generative models (flow matching, diffusion), simulation-based inference, and robust and active learning. The group has a wide network of
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stochastic processes, Markov models, dynamical systems, quantum walks, or related mathematical approaches. Experience with computational model fitting, Bayesian inference, simulation, or formal model
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working experiences, and professional mentorship and support. The postdoctoral associate will be affiliated with the Center for Avian Population Studies (CAPS, https://www.birds.cornell.edu/home/center
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of multi-modal healthcare record data. The ideal candidate will additionally have experience: Multi-modal AI model development Statistical modelling techniques (Bayesian inference, differential equations and
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data science approaches includes the application of Bayesian inference or probabilistic machine learning to geophysical models. UiO is subject to the Security Act, which governs the organisation's
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via nonlinear parametrizations such as deep networks, dynamical systems and control, Bayesian inference and generative modeling, and randomized linear algebra. Applications of interest are transport
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data science approaches includes the application of Bayesian inference or probabilistic machine learning to geophysical models. UiO is subject to the Security Act, which governs the organisation's
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-experiments for research purposes, Knowledge of online experiment, task and/or survey platforms (e.g. Gorilla, Prolific, etc.), Awareness of time-series, multilevel, Bayesian, or causal inference analysis