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benchmarking. Assess the framework with time-aware validation protocols and probabilistic error metrics, benchmarking it against individual experts and standard ensembles across pandemic phases and scenarios
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of neural networks, information-theoretic and optimal-transport perspectives on representation and generalisation, probabilistic numerics and Bayesian deep learning, and emerging frameworks for scientific
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on the design and development of mathematical, probabilistic, and statistical frameworks for drawing inferences from complex biological data in collaboration with scientists at the Snow Centre for Immune
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Experience in one or more of the following areas: object detection and segmentation, multi-object tracking, time-series analysis, probabilistic modeling and uncertainty quantification, real-time or streaming
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University of Massachusetts Chan Medical School | Worcester, Massachusetts | United States | about 2 months ago
. • Biomedical background NOT required. Preferred: • Experience in network inference, causal inference, network science, dynamical systems, systems science (e.g. systems biology), probabilistic modeling
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applications. This includes, but is not limited to, stochastic differential equations, stochastic partial differential equations, variational and geometric methods, probabilistic numeric, optimal transport, and
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workflows, and differentiable or probabilistic modelling approaches, to support robust calibration, sensitivity analysis and uncertainty quantification; abstract biological mechanisms into reusable design
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We are seeking to appoint a Senior Postdoctoral Researcher in Statistical Machine Learning and Deep Generative Modelling to apply and develop cutting- edge deep generative probabilistic models
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students. The required qualifications are: PhD degree in mathematics, science, engineering, or a related field by the start date. Extensive experience in one or more of the following areas: probabilistic
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/embeddings, probabilistic graphical models, or causal/relational inference. Background or strong interest in meta-research / scientometrics / scholarly communication, including citation-based analysis, claim