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data acquisition. Experience with advanced statistical or computational methods, such as mixed-effects models, hierarchical models, Bayesian models, trial-level analyses, dimensionality reduction
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device-relevant properties Design active learning, Bayesian optimization, uncertainty-aware modeling, and other adaptive experimental design workflows to guide experiments and improve data efficiency in
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experiments addressing the project’s research objectives, including developing experimental methods where appropriate. Process, interpret and manage research data, and contribute to integrating laboratory
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Experience in one or more of the following areas is preferred: Statistical genetics Human genetics Population genetics Evolutionary genetics Bayesian statistics Machine learning Large-scale genomic data
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systems, and multi-objective optimization. Prof. Freja Nygaard Rasmussen – life cycle assessment Prof. Sebastien Gros – decision-making, energy use, AI and machine learning Dr. Signe Riemer-Sørensen - AI
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, laboratory, and population sciences investigators at Huntsman Cancer Institute. Cancer Biostatistics provides expertise for cancer researchers, from formulation of research objectives to study design, along
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, Bayesian inference, model calibration, and Markov Chain Monte Carlo methods, uncertainty quantification, statistical modelling, and Gaussian processes, machine learning for time series, sequence-to-sequence
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Center for Drug Evaluation and Research (CDER) | Southern Md Facility, Maryland | United States | 2 days ago
well as Bayesian borrowing can help address sample size and ethical concerns for rare diseases. Collaboration between the statistician at DB9 and the clinical team at the Division of non-malignant hematology to map
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considered. Technical expertise and deep knowledge of one or more of the following areas: Physics-informed neural networks (PINNs) & surrogate modelling Time-series modelling & anomaly detection Bayesian
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networks, Bayesian inference, computational neuroscience, mathematics.