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Field
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& Technology. Applicants should hold a doctoral degree in public health, epidemiology, or a related discipline, and have strong experience in longitudinal data analysis & advanced causal inference methods (e.g
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, XRF, isotopic, and tephra analysis, alongside the construction of Bayesian age-depth models using radiocarbon, 210Pb, and tephrochronology. Candidates with experience in metagenomics (sedimentary aDNA
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radio interferometry data, particularly very long baseline interferometry. Experience with or skills relevant to statistical modelling and Bayesian inference. Demonstrated familiarity with the fields of X
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: 16133 School: Faculty of Arts and Sciences Position Description: We seek a postdoctoral research associate to work with Professor Michael Desai at Harvard University on projects involving inferring
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to protocol development, power/sample size considerations, and statistical analysis plans develop and apply advanced modelling approaches (e.g., survival and competing-risk models, causal inference methods
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effectively and efficiently with large scale electronic healthcare data (such as CPRD, MINAP, HES) applying advanced statistical modelling and causal inference techniques. What we offer in return 26 days
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-graph inference. Ensure the system is deployment-ready by supporting benchmarking of inference speed, compute efficiency, and scalability with concurrent agents. Maintain high software engineering
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physics-based insights with data-driven methods—such as physics-informed neural networks, surrogate models and Bayesian optimisation—to explain formation behaviour, identify early indicators of cell
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Max Planck Institute for Political and Social Science, Göttingen | Gottingen, Niedersachsen | Germany | 23 days ago
quantitative methods, such as survey-based and observational causal inference designs, with qualitative approaches, such as fieldwork and interviews. Through this research, the department aims to develop novel
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or translational research experience Knowledge of machine learning, Bayesian modeling, or statistical method development Ideal Personal Attributes: Independent, proactive, and scientifically curious Detail-oriented