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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict
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models (DDM, sequential sampling, Bayesian models). Experience with computer vision tools (e.g., MediaPipe, OpenPose, homography estimation, optical flow). Experience with eye-tracking data collection
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with advanced statistical techniques (optimal Bayesian, Markov Chain-Monte Carlo, etc.) to solve the forward and inverse problems involved. Additional information about AGAGE, CS3, and MIT atmospheric chemistry
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estimation of adverse Zika outcomes while addressing measurement biases. We aim at generating evidence to inform public policy, healthcare providers and pregnant individuals, improving prevention during
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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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models and algorithms, particularly within Bayesian, generative, or probabilistic machine learning frameworks, together with deep knowledge of causal inference, prognostic modelling, and individualized
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parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing; additionally other methods such as simulation-based inference Good computing skills
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contribute to the excellence of our academic community. We are looking for a postdoctoral researcher with expertise in Bayesian hierarchical spatio-temporal statistics and measurement error methods for a 3
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computing Excellent communication and collaboration skills Preferred: Experience with simulation-based inference and Bayesian methods Familiarity with cosmological simulations or observational cosmology ML
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of the following topics will be appreciated, but mostly we look for smart people who enjoy learning new things: Approximate Bayesian inference Differential geometry Numerical computations (ideally with experience in