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network performance data obtained from user devices. Assist in the development of basic models to predict or explain network behaviour under different conditions. Contribute to the improvement of internal
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, programming, and execution of behavioral, fMRI, EEG, and MEG studies, analysis and archiving of data collected from these studies, subject recruitment, and some administrative work. Depending on qualifications
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Your Job: Investigate current challenges and bottlenecks in power flow analysis for large scale electrical distribution grids Apply machine learning/AI or surrogate modeling (e.g., neural networks
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About the job: We are recruiting an enthusiastic, highly motivated individual with proven business and technical knowledge to lead business analysis, planning and reporting activities within AMIC
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tracking error. The aim is decision-grade uncertainty quantification (UQ) and principled data-driven parameter selection. Hence, the project will develop automatic portfolio rebalancing driven by UQ analysis
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analysis) to compare brain responses with predictions of computational models (deep neural networks developed by the NASCE team). The objectives include assessing how the brain segments, groups
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test facilities with regard to the research and project objectives Collaboration in the analysis of operated SOC stacks through disassembly and post-test characterisation to identify material
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the modelling, simulation and analysis of the large, complex and dynamic systems (e.g. cyber and physical systems). The PhD student will join the Sheffield Control and Power Systems (CAPS) Laboratory (https
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Analysis for Sustainable Agriculture All details are avaiable here : https://www.eu4greenfielddata.eu/content/download/198/2088?version=3&nb… ; EU Recruiting institutions Université Toulouse Capitole
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https