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will deliver projects that leverage large-scale electronic health record data and rich cytometry data derived from full blood count analysers to develop and refine machine learning models to improved
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to predict injuries for the whole population, in collaboration with industry, academia, authorities, and insurance companies. Main responsibilities Computational Modeling and Simulation Develop and validate
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systems Strong skills in data-driven analysis and modelling, simulation, control, and validation Familiar with modeling of PtX and storage technologies, model predictive control, machine learning
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are inherently social beings. We predict how others will respond to our own actions and coordinate our own and others’ behavior based on these predictions in order to live in harmony with those around us
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The Division of Vehicle Safety studies accidents, driver reactions, and injury mechanisms. The Injury Prevention group develops Human Body Models to predict injuries for the whole population, in collaboration
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execution time (WCET). The postdoc position focuses on compiler support for WCET analysis for time-predictable architectures such as Patmos/T-CREST. Furthermore, it is expected to join the development of a
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation increments, which represent structural model errors (https://doi.org/10.1029/2023MS003757
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) is sought to join a large organ-chip research team within Queen Mary’s Centre for Predictive in vitro Models. This project, entitled, “Micro-manufacturing of tissue patterned organ-chips
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Python) Experience in analyzing large and/or complex datasets Interest in quantifying uncertainties for computer models and/or climate predictions Ability to work in a team Ability to communicate orally in
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Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO | Grenoble, Rhone Alpes | France | 10 days ago
multi-expert segmentation databases. The postdoctoral fellow will focus on integrating segmentation variability into deep learning models, with the goal of assessing prediction reliability and enabling