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Sharing – Building a federated data space to enable responsible data integration and cross-project learning. AI & Modelling – Using shared data to power advanced models that help describe and predict
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other machine learning models. Generate and evaluate hydrologic hindcasts and forecasts to assess model fidelity, forecast reliability, and predictive skill across subseasonal to annual time scales
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institutes and an industry partner. Your task is the build-up of a predictive model for tandem cell stability. Your tasks in detail: You receive tandem solar cells from a partner institute and perform high
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of funding. The MLE will work on projects that will include applications of neural networks to efficacy and potency prediction for drug combinations using multimodal datasets including molecular features
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project working to develop real-time vector-borne disease risk assessment in low resource areas. The individual will be directly responsible for the development of adaptive predictive models for nowcasting
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We are a German and Portuguese start-up revolutionizing novel and high impact materials discovery using leading AI models and smart synthesis. At alqem, we believe that new breakthrough materials are a
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predictive modelling; Bioinformatics and Knowledge Graphs (visualization and reporting); AI-based data integration across cohorts (with federated machine learning); Contribute to ongoing projects, such as: o
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Infrastructure? No Offer Description This project is part of the European ERC Synergy project Karst https://erc-karst.eu/ , which aims to develop a predictive flow model for an entire karst network. We will
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skills are required for the job: - Computational modeling of molecular crystals. - Computational and theoretical chemistry. - Crystal structure prediction. - Familiarity with the Linux operating system
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. Extensive knowledge of statistical methods including multivariate and univariate analysis of large data sets, learning and predictive modeling, network analysis, and probabilistic approaches to test theories