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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
between process parameters and material properties will be developed and subsequently exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material
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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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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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. Gaussian Process Regression) model to describe the relationship between process parameters and material properties will be developed and subsequently exposed to Bayesian optimization to find the optimal set
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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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–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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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 3 hours ago
process in a more systematic way. Science systems engineering focuses on ensuring alignment between the design and operation of an engineered system and its top-level science objectives, but there has not
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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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: Genetic and developmental mechanisms of cardiovascular disease. Goals: Discover causal variants, understand congenital heart disease, and advance pharmacogenomics. Heart Failure Focus: Mechanisms and