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Field
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the surrogate forward models with a Bayesian inverse modeling framework to achieve real-time or near-real-time uncertainty quantification, such that we can efficiently resolve the uncertainties rising from rock
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deep experience with PyTorch, JAX, or TensorFlow. Broad knowledge of modern ML and optimization (gradient‑based, evolutionary, Bayesian, reinforcement learning). Hands‑on experience with generative
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quantification, including Bayesian approaches and stochastic methods. Advanced knowledge of sequential and time-series modelling frameworks, including state-space models and modern deep learning approaches
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teaching expertise in several of the following areas: Bayesian learning and probabilistic modelling Multimodal and integrative analysis, including multi-omics settings Data visualisation and exploratory
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the mismodeling of gravitational waves, of astrophysical environments, or of noise artifacts in gravitational-wave inference, The development of Bayesian data analysis techniques to carry out parameter estimation
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a Bayesian perspective, the use of skew error distributions from well-known skewing mechanisms (eg Azzalini, 1985; Ley 2010), while allowing the parameter(s) controlling the skewness to vary over time
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health data; knowledge of infectious disease transmission dynamics; and competence in applied Bayesian statistical modelling. Strong quantitative background, with demonstrated ability to program in one
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Bayesian optimization and other active learning techniques to guide experimental efforts by identifying optimal chemical compositions and processing conditions of membranes that maximize both selectivity and
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mieux comprendre comment ces objets façonnent le MIS des galaxies
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this professor position might be for you. We are looking for a new professor to lead research in probabilistic machine learning, with a focus on areas such as deep generative models, Bayesian methods