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técnicas de machine learning e IA. Sólidos conocimientos en análisis y tratamiento de datos, programación y desarrollo de modelos analíticos. Experiencia con bases de datos y herramientas de análisis
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-level modelling of environmental exposures and health risks; Interpretable and uncertainty-aware machine learning for heterogeneous health data. This position offers the opportunity to work in a
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signals, imagery, and natural language, via adaptive machine learning; 2) Personalization of sequential decision making, biophysical digital twins, and the operation of digital and physical systems; and 3
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to the development of remote sensing algorithms, simulation and modeling capabilities, and artificial intelligence/machine learning (AI/ML) methods in support of the goals of the joint NASA/USGS Landsat mission
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modelling (e.g., LSTMs) and deep generative/unsupervised anomaly detection techniques (e.g., VAEs). *Strong programming proficiency in Python and familiarity with standard data science and machine learning
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to the mathematics of plasmas, with a focus on kinetic theory and PDEs. - Specific topics include stability, long-time behaviour, and mathematical properties of magnetised plasma models (such as magnetized
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, machine learning, generative AI and related fields as part of the DataSig II grant “High order mathematical and computational infrastructure for streamed data that enhance contemporary generative and large
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microscopy. • Expertise in metabolomics with mass spectrometry is desired. • Strong general computer skills, experience with databases and scientific applications, and ability to quickly learn and master
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state modeling, and machine-learning methods to characterize the conformational dynamics and activation mechanisms of signaling proteins and other therapeutic targets, and to translate these mechanistic
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to have published in leading machine learning conferences or similar venues. One or two PDRAs will be recruited to work within one of, or across, the four research themes: Learning with Structured