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, Neuroscience, Physics or a related field. Candidates should have strong skills in machine learning and statistics and experience with Gaussian process regression and/or probabilistic regression. Experience with
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efficient simulators for continuous and discrete variable systems with noise based on Gaussian/stabilizer decompositions quantum error mitigation and correction for continuous variable systems evaluating and
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
produced by PBF-LB. After identification of the most relevant parameters adopting a design of experiments strategy, a probabilistic (e.g. Gaussian Process Regression) model to describe the relationship
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strategy, a probabilistic (e.g. Gaussian Process Regression) model to describe the relationship between process parameters and material properties will be developed and subsequently exposed to Bayesian
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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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design of experiments strategy, a probabilistic (e.g. Gaussian Process Regression) model to describe the relationship between process parameters and material properties will be developed and subsequently
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on Gaussian/stabilizer decompositions quantum error mitigation and correction for continuous variable systems evaluating and rigorously define the computational power of continuous variable systems Your Profile