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research problems. They will specifically apply advanced methods for data analysis and modeling, such as community detection and link prediction. Beyond direct research, the incumbent will assist in
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efficiency and lifetime predictions under realistic operating conditions. Validating the developed models using experimental data from drivetrain test benches equipped with load, temperature, vibration, and
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to the analysis of multi-omic data, models for predicting phenotypes using genotype data, biological data integration, etc. Participation in these projects will include scientific programming, data analysis
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into European energy system models based on the institute's own open-source FINE framework https://github.com/FZJ-IEK3-VSA/FINE . Your tasks in detail: Implementing geothermal plants with material co-production
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-Preserving Federated Learning: Establishing secure, decentralised architectures for training predictive models on sensitive medical and industrial datasets without compromising data integrity. Propelled by
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of models like CNN, RNN, Transformers with some work in classical machine learning with XGBDTs is expected. Relevant work can lead to co-author publications and contributions to grant proposals. Tentative
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
to perform disease modeling and critical analytics in response to infectious disease outbreaks. Duties will include helping to implement predictive and analytic models of infectious disease using Python and R
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, product management work, and and leadership responsibilities • Familiarity with artificial intelligence and machine learning approaches, including predictive modeling and precision analytics applied
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software, including design tools, CAD drafting, information systems, construction management software, computerized maintenance management systems (CMMS), hydraulic modeling, Bluebeam, and the full Microsoft
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information from clinical notes ? Implementing machine learning models for prediction and classification tasks in cardiovascular populations ? Cleaning, preparing, and managing large healthcare datasets