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metapopulation and/or individual based models Knowledge of Bayesian methods, including Approximate Bayesian Computation Experience with big data analysis and HPC environments Knowledge of additional programming
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and statistics, with expertise spanning time series analysis, Bayesian inference, financial econometrics, and data analytics. As home to one of the strongest forecasting research groups worldwide, we
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targets and biomarkers. Study cardiomyopathy across models of diabetes, cancer, and pressure overload. Collaborate in an interdisciplinary team and contribute to high-impact publications. Your
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will be adapted to the candidate’s background and the evolving needs of the center. Possible directions include the application of rock physics models, Bayesian inversion methods, and machine learning
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physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or equivalent) in
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/drawbacks. Experience with Bayesian statistics a plus. Experience with censored datasets a plus. Proven record in writing successful research proposals. Demonstrated ability of working in a multidisciplinary
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. Probabilistic rational models, implemented as either Bayesian models or deep neural networks, have been proposed as standard models, from low-level perception and neuroscience to cognition and economics. But
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dates Materials submitted by September 15, 2025 will receive full consideration. Applications received after this date will be reviewed on a rolling basis. The target start date is January 1, 2026
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in neural interfacing. Projects involve peripheral nerve regeneration, mechanoreceptor-specific targeting, and surgical reconstruction for prosthetic interfacing. Deadline for application: Apr 15, 2025
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. Depending on available funding, one of the positions may be specifically targeted at candidates interested in the scaling regimes of deep networks. Basic Qualifications A PhD is required. We seek candidates