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: It is advantageous to have experience in one or more of the following areas: -Machine Learning & Bayesian optimization (Python, Supervised learning, Multi-objective optimization) -Additive
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Menopausal Women” with full-time employment for a duration of 3 years, starting in February 2026. Objective of the project: BrainAGE is a machine learning-based biomarker that estimates biological brain age
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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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Catalunya, the Autonomous Government of Catalonia, and the Universitat Autònoma de Barcelona (UAB, a public university) whose main objective is to carry out research and to contribute to the development
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double affiliation with the Centre for Educational Measurement (CEMO). CREATE is an interdisciplinary Centre of Excellence funded by the Research Council of Norway with the objective to generate novel
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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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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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, 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