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involving large companies under the “Autonomous Systems, Robotics, and Machine Tools” track of the IASMIN Platform (a FAPESP Applied Research Center in AI). The work is to be carried out on-site
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machine learning algorithms to support the traceability of the beef’s geographical origin. She/he will participate in all stages of the project, including planning and supervision of sample collection and
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computing, obtained within a maximum of 7 years; application of machine learning and deep learning methods to remote sensing images; proficiency in programming (R, Python, or similar); ability to work
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position within a Research Infrastructure? No Offer Description Activities and context: The fellow will develop machine-learning interatomic potentials (MLPs), trained on density functional theory-DFT data
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chromatography. The candidate will prepare samples for immunopeptidomics by liquid chromatography coupled with mass spectrometry tandem (LC-MS/MS), analyze large datasets, and develop scripts and machine learning
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in tropical regions; analyze links between macrofauna and soil carbon; build/validate scoring algorithms using machine learning/cumulative functions. Outputs – Lead scientific, technical, and policy
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; • Familiarity with statistical analyses and modeling as well as machine learning approaches, supported with strong skills in computational optimization of methods; • Experience working with large-scale datasets
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to staff position within a Research Infrastructure? No Offer Description Activities: The post-doctoral researcher will develop machine learning models to evaluate the effectiveness and cost
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Experience in plant breeding for resistance to biotic and/or abiotic stresses, programming, machine learning, and genomic data analysis. Desirable requirements 1. Cost-effectiveness assessment: rapid
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on items directly related to the research activity. Where to apply Website http://www.fapesp.br/oportunidades/9615 Requirements Additional Information Eligibility criteria Eligible destination country/ies