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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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-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi
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to identify trends, variances, and opportunities for improvement. Support the development of long-term financial strategies and risk assessments. Builds predictive models and forward-looking analyses
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, based on detailed studies of Earth and the solar system, is developing predictive models to identify habitable planets around other stars. Within three different research themes: (1) Planets and Early
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at the Institute of Theoretical Astrophysics at Blindern, Oslo. Job description Constraining gravity and cosmological models using the Euclid survey, supervised by Assoc. Prof. Hans Winther. The Euclid
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interest in social science applications, and with strong competence in statistics and machine learning. The successful candidate will develop predictive models using machine learning and work alongside other
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contexts. This work will directly support the development of AI models to predict off-target effects across clinically relevant cell types, including primary cells and 3D organoid systems. Responsibilities
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, project and program evaluation, and report writing. Data science and Geospatial Analysis skills, including coding (e.g., Python, R), inferential statistics (e.g., MATLAB, STATA), predictive modeling, GIS
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Join us for an exciting Doctoral student journey that will combine systems biology, computational modeling, and industrial biotechnology to solve a key challenge in sustainable biomanufacturing
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significant computational resources for centralized processing. Second, the existing centralized, terrestrial-based control infrastructure cannot scale with the increasing number of airborne sensors due