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Field
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, multidimensional datasets is transforming marine ecology and redefining how we detect and respond to ecosystem change. Methodology This PhD will place you at the forefront of this emerging field. You will address a
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Areas (Codes 25–29) 1. Machine Learning (Code 25) Objectives: Support UFABC’s undergraduate and graduate programs, strengthen research in Machine Learning, and expand English-taught course offerings
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exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised
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for Educational Measurement (CEMO). CREATE is an interdisciplinary Centre of Excellence funded by the Research Council of Norway with the objective to generate novel knowledge about how to reduce inequalities in
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–frequency representation (local stationarity, correlations, textures, extrema, anisotropy). This approach suggests leveraging the entire representation to define more robust detection and tracking criteria
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of knowledge-driven models, leveraging Bayesian statistics and causal inference for calibrated uncertainty, distribution-shift detection, and safety guarantees. You will be will working within the Center
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of knowledge-driven models, leveraging Bayesian statistics and causal inference for calibrated uncertainty, distribution-shift detection, and safety guarantees. You will be will working within the Center
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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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) for engineering systems and structures, as well as expertise in machine learning, stochastic modeling, and Bayesian statistics. Programming Skills: Proficiency in programming languages such as Python, C, or R
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, stochastic modeling, and Bayesian statistics. Programming Skills: Proficiency in programming languages such as Python, C, or R. Teamwork and Responsibility: Ability to work effectively within a project team