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, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI
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and/or case-crossover analyses, and have a promising publication record. Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization
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: Within this international project, TU Delft will develop a machine learning-based forward operator to enable the assimilation of SAR imagery into the crop growth model. You will: Process SAR imagery over
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generating fusion energy. This research will focus on the chemical speciation and transport of tritium in the molten salt blankets using ab initio quantum simulations, machine learning potentials, and
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machine-learning and AI methods for complex engineering and industrial systems, with a particular focus on improving their reliability, availability, and operational performance while enabling more
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and meteorological modeling. Experience running or maintaining operational/automated forecast systems. Experience applying machine learning methods to air quality or geophysical data (e.g., forecast
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. • Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization, and a high level of independence with a publication record to support these
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backgrounds in adjacent areas and a firm commitment to learning glycobiology approaches are also encouraged to apply. Interested candidates should include a CV, brief statement of research interests, and
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evolution. ● Experience with population genetics. ● Experience developing R packages. ● Experience applying or developing machine learning tools. ● Strong scientific writing skills. Brief
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. machine learning, computer science, mathematics, statistics, physics, theoretical neuroscience or a closely related field) with significant post-qualification research experience. You will experience