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transcriptomics. He is the lead developer of widely used open-source software including BayesPrism, a Bayesian deconvolution framework selected as a Nature Cancer 2022 highlight. Dr. Chu's research has been
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and Causal Reasoning: Strong statistical foundations including hypothesis testing, Bayesian inference, uncertainty quantification, and causal modeling for biomedical data. Biomedical Data at Scale
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advanced statistical methodologies, including several of the following: Survival analysis Hierarchical and mixed-effects models Clinical trial design and analysis Structural equation modeling Bayesian data
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anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and simulation Demonstrated experience applying AI to the SIT’s
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Research Engineer/Postdoctoral Position Decision and Bayesian Computation (DBC) – Epiméthée (EPI) Laboratory Institut Pasteur, Paris | 25 rue du Docteur Roux, 75015 Paris Position Overview We
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ongoing research on mechanistic modelling and Bayesian parameter inference from in vitro neural models. Combine theory, simulation, and data-driven methods, this in close interaction with the researchers
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workflow driven by a Bayesian optimization engine — translating algorithmic experimental directives into machine actions and returning structured results to the decision-making system. Responsibilities
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interests in the areas of applied and pure mathematics, and statistics. In Statistics, the School has research strengths in Bayesian and Monte Carlo Methods, Biostatistics and Ecology, Combinatorics, Data
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. Proficiency in Python, MATLAB, or R. Strong quantitative and analytic skills. Preferred Qualifications Experience with evidence-accumulation models (DDM, sequential sampling, Bayesian models). Experience with
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robability Theory, Stochastic Analysis, Mathematical Statistics, Bayesian Statistics are encouraged to apply. Responsibilities for these positions include mathematical research, undergraduate and graduate