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Field
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-funded CIRCUIT project (Causal Inference for Resilient Cities and Urban Integrated Transport), a four-year interdisciplinary research programme at Trinity College Dublin. The project develops advanced
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advanced data science methods, including statistical modeling and machine learning approaches Developing and applying novel causal inference methods, such as target trial emulation and causal AI frameworks
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, i.e. interconnected ecosystems). Recent developments have indeed sought to establish the link between scales using Bayesian dynamic networks (Trifonova et al. 2025). This article proposes a strategy
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Sustainable Healthcare Decisions. About the project The fellowship period is 3 years and devoted to carrying out a project entitled “Reliable Bayesian prediction models with uncertainty quantification
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“Bayesian Enhanced Tensor Factorization Embedding Structure (BETTER)”, and this PhD project specifically aims at developing a unified, scalable, and interpretable framework for tensor analysis. Specifically
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. mixed effects regression models and/or Bayesian statistics; e.g., brms / lme4 packages). Excellent written and spoken English. Desirables (traits that would give you an advantage) Training in evolutionary
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or more of the following: ecological modelling, dynamical systems, network analysis, Bayesian statistics or probabilistic modelling, mathematical biology, multivariate data analysis. Interest in connecting
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French National Research Institute for Agriculture, Food and Environment (INRAE) | Toulouse, Midi Pyrenees | France | 22 days ago
conferences. Supervision Dr Arthur Kocher (IHAP; MPI-EVA) is the PI of the EpidemioCene project . He studies human and animal disease ecology and evolution using molecular approaches combined with Bayesian
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Institut de Recherche pour le Développement (IRD) | Sete, Languedoc Roussillon | France | 5 days ago
analysis, gravity models, Bayesian models, etc.). In this regard, proficiency in software is required: programming languages such as R or Python, machine learning, econometric softwares, data management
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algorithms that combine Reinforcement Learning techniques like Partially Observable Markov Decision Processes (POMDPs) with cognitive inference modules capable of modelling human beliefs, intentions, and goals