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                applicants. Application: Send CV, letter of interest, and 2 references to Julio C. B. Ferreira (jcesarbf@usp.br ). More details: fapesp.br/en/postdoc . More information about the fellowship is at: fapesp.br 
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                includes a monthly stipend, access to UNICAMP’s healthcare system, and research contingency funds. For more information on candidate requirements, visit FAPESP's page at https://fapesp.br/en/postdoc 
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                proteomics, characterizing the biological processes and cellular functions related to differentially abundant proteins (DAPs). The postdoc will be based at the Sepsis Research Lab (LPS) of the Federal 
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                related to staff position within a Research Infrastructure? No Offer Description 18-month fully-funded postdoc position to work on a project investigating the protective effects of carnosine on cardiac 
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                (R3) Positions Postdoc Positions Country Brazil Application Deadline 3 Oct 2025 - 23:59 (America/Sao_Paulo) Type of Contract Not Applicable Job Status Full-time Hours Per Week 40 Offer Starting Date 1 
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                ” project networks; fostering dialogue and appropriation among the project’s research network; integrating different forms of knowledge and epistemologies (hegemonic and non-hegemonic); and leading creative 
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                this dataset and integrate it with literature data, aiming to build a comprehensive view of galaxy formation and evolution. The position starts on January 1, 2026, for 18 months. The postdoc will work with the 
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                , uncertainty, and digital platforms; - Methods: Gioia, Langley, Eisenhardt, CNA, QCA; structural equation modeling, neural networks; - Fluent communication in Portuguese and English is considered a plus. The 
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                ) research network and to broaden the academic and social impact of the project. This opportunity is open to candidates of any nationality. The selected candidate will receive a Post-Doctoral fellowship from 
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                planets, and trans-Neptunian objects in retrograde motion. Traditional orbital dynamics methods will be combined with machine learning techniques, such as convolutional neural networks and Vision