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in applied Machine Learning and Information Retrieval, commencing 1 August 2025 or as soon as possible thereafter. The position is part of the research project: A digital optical computing platform
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available within the Department of Medicine, Division of Endocrinology in the Laboratory of Brain Behavior and Big Data at the University of Southern California. The applicant with work with PI and
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the fundamental understanding of pesticide residues in soils through experimentation, modelling, and big data analysis. You will: Combine existing farm management, soil property and soil pesticide residue data
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test planning, instrumentation (e.g., strain gauges, LVDTs, DIC), execution of large-scale tests, and data analysis. Solid understanding of structural behavior, failure mechanisms, and durability issues
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tools to differences in genotypes and environment to predict disease trajectories. The project is part of a Nordic initiative in large scale healthcare data analysis for development of precision medicine
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of substitution models using large dataset, successful applicants must then have a PhD and demonstrated experience in discrete choice models, machine learning techniques, big data, and optimization
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The candidate will have a PhD or equivalent degree in bioinformatics, biostatistics, computational biology, machine learning, or related subject areas Prior experience in large-scale data processing and
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to the ongoing research in Prof. Marcotti’s laboratory by designing, developing and performing experiments, data analysis and disseminating the findings by writing articles and by presenting findings
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. Required Qualifications: Doctoral degree (PhD) conferred by start date Demonstrated experience with analysis of large health databases Training and experience in machine learning and deep learning methods
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detection framework for tipping points. Contribute to the design of scalable and interpretable forecasting strategies for large climate simulators, integrating adaptive sampling and Bayesian techniques