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non stationnaires. Dans ces représentations (STFT/ spectro- gramme, ondelettes, etc.), les composantes d'intérêt apparaissent sous forme de ridges. Estimer ces ridges suffit alors à reconstruire les
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of parametrization of these models based on least squares and Bayesian calibration techniques employing longitudinal series of anonymized PSA data from patients. 3) Analysis of the predictions, parameters, and
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Centre (NCN). The Principal Investigator is Dr. Eng. Piotr Kopka, email: Piotr.Kopka@ncbj.gov.pl Project description: The project aims to develop a new class of inverse Bayesian models called STE-EU-SCALE
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design, such as Bayesian Adaptive Clinical trial design or established expertise in statistical methods such as structural equation modeling, causal data analysis. Experience in serving in protocol review
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QUANTITATIVE METHODS and is part of a cluster hire across the School of Social Sciences. The specialty area should be in human factors/human-computer interaction (HF/HCI), industrial-organizational psychology
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this position will continue with their faculty appointment at The Cooper Union. Essential Functions/Responsibilities Expertise in all areas of machine learning including deep learning, Bayesian statistics
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experimental methods. Develop and apply methods for demultiplexing, normalization/QC, effect-size estimation, biological inference, and predictive modeling. Contribute to biological manuscripts and methods
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
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parameter estimation using Bayesian inference, and/or the exploitation of Machine Learning (ML) based algorithms to reduce false positives caused by human generated interference signals in the observational
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(for example, R, Python, or Matlab). Experience with graph modeling, Bayesian statistics, or causal inference is a plus. The candidate will join an integrated team of computational scientists, molecular