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available for full-time dedication to the project. Desirable requirements: PhD completed recently, preferably within the last two years, or currently in the final stage of completion. Experience in microalgal
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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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metadata (location, climate, soil properties) into the GlobalSoilMacrofauna format. Deposit datasets in open-access cloud/local repositories. Data Analysis– Explore drivers of macrofauna abundance/diversity
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article writing will be carried out concurrently. Mandatory requirements This project is suitable for a highly motivated candidate with experience in the synthesis and characterization of nanomaterials, as
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individual will join a multidisciplinary team at CQMED—set up at the State University of Campinas (UNICAMP) and supported by FAPESP through its Partnership for Technological Innovation (PITE) program—and will
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, statistical analyses (including multivariate approaches), and scientific writing. Mandatory requirements: PhD in physiology, ecology, neuroendocrinology, molecular biology, or related fields. Strong experience
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physicochemical characterization; ability in experimental design, data analysis, and scientific writing in English; availability for full-time dedication in accordance with FAPESP regulations . Desirable
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writing skills in English. How to apply This opportunity is open to candidates of all nationalities. Applicants must submit the following documents merged into a single PDF file to the email jorgkoba
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recommendations; and publishing research findings in peer-reviewed scientific journals. Mandatory requirements: Ph.D. in Crop Science, Plant Production, Agronomy, or a related field, obtained within the last seven
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evaluation in cell models. Positions: 1 Duration: 12 months, with the possibility of renewal up to a maximum of 48 months, subject to FAPESP Postdoctoral Fellowship regulations and satisfactory performance