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WaterWeave project, which focuses on innovative solutions for monitoring and the sustainable management of water resources. The fellow will develop machine learning and cloud computing techniques to estimate
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-ranked university in Latin America, boasts a Computer Vision Group at IME-USP with over 20 years of experience in machine learning research and strong international collaborations. ** Application Process
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. The fellowship candidate must hold a Ph.D. in Agricultural Engineering or related fields, with expertise in irrigation, remote sensing, image processing, and precision agriculture. Experience in programming
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expertise in advanced immunology, histology, flow cytometry, confocal microscopy and image analysis techniques. Experience in different national and international laboratories is a positive aspect to be
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expertise in advanced immunology, histology, flow cytometry, confocal microscopy and image analysis techniques. Experience in different national and international laboratories is a positive aspect to be
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kidney injury, providing deeper insight into the cellular processes that support renal immune function. Candidates must have a proven record of scientific achievement during graduate and/or postdoctoral
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machine learning (ML) algorithms to identify previously unknown correlations between synthesis parameters (inputs) and optical, electronic and chemical properties (outputs), such as quantum yield, light
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and computer programming skills. The objective of this research is failure and damage detection in the petroleum artificial lift process using the operational data signal analysis (time series analysis
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). The position offers the opportunity to interact with international collaborators and industry partners. The fellow is expected to explore and design solutions based on machine learning (ML), as well as the use
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. The project aims to validate and standardize use of advanced, minimally invasive imaging modalities as complementary tools to conventional necropsy for diagnosing lesions and determining causes of death