55 developer-"https:" "https:" "https:" "https:" "https:" "CSIC" Postdoctoral positions in Brazil
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recipient will contribute to the core research theme of the RATIONAL project, which focuses on investigating and developing robust evidence-based retrieval methods for Natural Language Inference (NLI) tasks
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(~US$ 2.400). FAPESP offers relocation assistance for researchers residing outside the area who need to relocate. Activities include conducting Research and Development in the area of AI applied
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Foundation's São Paulo Business Administration School (FGV EAESP), in cooperation with KU Leuven (Belgium). Activities include research design, literature review, development of research instruments, interviews
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area: Development of bioinspired antioxidants based on betalain molecular scaffolds; reactivity toward reactive oxygen/nitrogen species and electronically excited (triplet) states; photobiological
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: For the application, fill out the online form at: https://forms.gle/9hkPcnEwJPdefjXc9 . Deadline: September 15, 2026. Where to apply Website http://www.fapesp.br/oportunidades/9702 Requirements Additional Information
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be spent on items directly related to the research activity. Where to apply Website http://www.fapesp.br/oportunidades/9775 Requirements Additional Information Eligibility criteria Eligible destination
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be spent on items directly related to the research activity. Where to apply Website http://www.fapesp.br/oportunidades/9778 Requirements Additional Information Eligibility criteria Eligible destination
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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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with agronomic traits, with an emphasis on resistance to fungal diseases in grapevine, aiming to develop biotechnological tools and enhance disease resistance. The candidate should have knowledge and
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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