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at: https://forms.gle/qxf5vo5kKmf9sBNV7 . Mandatory requirements: Bachelor’s degree in Biological Sciences and a PhD in Biotechnology, either completed or with the defense scheduled before the start of
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(including inventories, geomorphological and susceptibility maps, management and contingency plans and operational protocols). Mandatory requirements: Candidates must have obtained a PhD within the last seven
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with state-of-the-art microscopy platforms; • Integrate structural and functional data; • Work in an interdisciplinary and collaborative environment. Mandatory requirements • PhD in Biology, Biological
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well as to the geometric optimization of the separator, including the swirl generator. Mandatory requirements PhD in mechanical, naval or aerospace engineering, or a related field. Proven experience in numerical simulation
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data and deep learning methods to assess canopy cover, quality, carbon stocks, and ecosystem services. Mandatory requirements: PhD in areas related to forest resources, remote sensing, data science, or
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, public land management, or urban and housing regulation. They must hold a PhD awarded within the past seven years, have an outstanding graduate academic record, be available for full-time, on-site research
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reports; publish high-impact papers; lead dataset deposits; organize partner workshops. Mandatory requirements PhD in Soil Science, Agronomy or Environmental Science; Solid experience with soil macrofauna
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to their Lattes CV, ORCID, and other relevant profiles; and (iii) A recommendation letter, preferably from the PhD supervisor, or from another referee familiar with the applicant's scientific and technical
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of the University of São Paulo (USP) in its Butantã campus. Mandatory requirements: Applicants must hold a PhD degree obtained within the past seven years and preferably have a background in architecture and design
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high-throughput datasets); • PhD in Bioinformatics, Computational Biology, or a related field; • Strong experience in bioinformatics and analysis of next-generation sequencing data; • Proficiency in