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Field
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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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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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content, merit, and instructor qualifications. Many of our courses may be applied toward degree programs or may be eligible for professional certificates and licenses. More information can be found at https
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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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Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field observations—including
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project “Actively learning experimental de-signs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate/index.html The PhD fellow will be part of a growing
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medicine and diagnostics, epidemiology and biology of infection. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/ The future of life science is data-driven. Will
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is home to a consortium of postdoctoral fellows who provide modeling expertise for a wide range of projects as integral members of those research teams. Unit URL https://imci.uidaho.edu/ www.uidaho.edu
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based on citizenship or visa status. Preferences Preferred • Experience using or modifying machine learning models such as decision trees, Bayesian models, deep learning architectures, transformers