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neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection. Comparison with known analytic methods and
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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | 7 days ago
for highly-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
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Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling
to test those predictions. By comparing model forecasts with genomic and phenotypic data from the evolving populations, you will test whether a deep understanding of the cell can inform predictions
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logistics providers, resilient forecasting and planning of logistics operations, management of sustainable logistics operations, next generation warehouse automation and others. The work will involve
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logistics providers, resilient forecasting and planning of logistics operations, management of sustainable logistics operations, next generation warehouse automation and others. The work will involve
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. Development of DT information modelling, data fusion, and forecasting guidelines and standards, and technology maturity benchmarks to derive cloud platform maturity level standards. Lead on the development
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, demographic modelling, and forecasting. Its research meets the highest international standards, demonstrated by publications in leading journals, presentations at global conferences, competitive external
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seeking exceptional researchers to join our dynamic team. As a Machine Learning Researcher, you will apply advanced ML techniques to a wide range of forecasting challenges, including time series analysis
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, the project will develop machine learning based solutions for predictive grid analytics (such as grid congestion forecast, asset monitoring, etc.). Based on these results, the project will develop
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to forecast disease burden under alternative vaccination schedules and to assess the impacts of completed vaccination programs against baseline transmission counterfactuals. This project will build on existing