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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP
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to offer. Qualifications: Required: PhD in ecology by start date Experience in plant phenology, biogeography, and spatial and temporal modeling (Bayesian and frequentist) Expertise in R or Python, GIS, big
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. Stéphane Bayen (McGill) and Dr. Sébastien Sauvé (Université de Montréal Target Hire Date: October 1, 2025 to August 31, 2026 Application Deadline: September 10, 2025 Remuneration: $48,000/year negotiable
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-development and refinement of conceptual models; devising management scenarios; building network models in one or more platforms (e.g., loop analysis/qpress; fuzzy cognitive maps/Mental Modeler; Bayesian belief
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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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mathematical background, including expertise in stochastic optimization (e.g. Markov decision theory and dynamic programming) and applied probability (Bayesian statistics). Excellent coding skills (e.g., in Java
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evidence in clinical practice guidelines and feasibility of generating evidence from target trial emulations). "CPG-EMULATE" is a research project funded by the French National Research Agency (ANR) and
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: developing and testing new approaches to water resources modelling, application of Bayesian inference methods to environmental problems, machine learning and data science applications, undertaking analysis and
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to implement advanced computational pipelines, including machine learning, deep learning, Bayesian inference, and probabilistic mixed membership modeling for innovative research. · Contribute