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emulators for accelerated forward modeling Advanced data-intensive machine learning and AI techniques for survey analysis Applications to major international surveys, including LSST (Rubin Observatory
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highly interdisciplinary setting combining microbial mutagenesis assays, mammalian cancer models, next-generation sequencing, bioinformatics, and machine learning. Experimental data will be integrated with
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information about us, please visit: www.dbb.su.se . Project description The candidate will develop machine learning (ML) strategies, primarily revolving around interpretable ML and generative AI, to study
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matter observatory. Main responsibilities The postdoctoral candidate is expected to focus on statistical data analysis including machine learning, Monte Carlo simulations, operations and calibration
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innovative development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. More specifically, at NRM this research will be