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Your Job: This research primarily seeks to incorporate advanced neuron models, such as those capturing dendritic computation and probabilistic Bayesian network behavior, into unconventional
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develop risk assessment methodologies for bridges and civil infrastructure, which integrate remote sensing data with physics-based models into a probabilistic decision support system. You will establish a
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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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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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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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PSPR optimization of recently developed lean Mg-0.1Ca alloy produced by PBF-LB. After identification of the most relevant parameters adopting a design of experiments strategy, a probabilistic (e.g
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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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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | about 2 months ago
-motivated and qualified candidates to work with an international team on developing cutting-edge novel demographic, statistical and computational methods in estimating, modelling and forecasting measures
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gravel pores and reducing oxygen supply to benthic habitats. These measurements explain the problem, but they cannot forecast future conditions or test management scenarios. Planners are therefore forced
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of hydrological models to reliably forecast extreme hydrological events. However, these data sets must initially undergo interpolation before integration into hydrological models. Current research is developing