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erectile dysfunction and spermatogenesis. The project will include studies in humans, animal models, and in vitro approaches. Requirements: • Degree in Biomedicine, Biology, Biomedical Sciences, Medicine
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models to improve seed-sowing strategies, assess long-term population sustainability, and support evaluations of cost-effectiveness and cost–benefit of the program. The project also emphasizes social
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dataset generation technique to optimize the training of neural networks (NNs) for seismic data prediction. The use of neural networks to predict seismic velocity models has shown increasingly accurate and
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Science, Computer Engineering, or a related field, completed before the fellowship start date. Candidates should demonstrate experience in at least two of the following areas: compilers, parallel programming
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vesicles (OMVs), exploring OMV-based cancer nanovaccines by using analytical approaches, cell culture systems, and animal models. The fellow will be based at CNPEM's Brazilian Biosciences National Laboratory
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23 Jan 2026 Job Information Organisation/Company FAPESP - São Paulo Research Foundation Research Field Engineering Researcher Profile Established Researcher (R3) Application Deadline 28 Feb 2026
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to staff position within a Research Infrastructure? No Offer Description The Laboratory for High-Pressure Technology and Natural Products (LTAPPN/ZEA) and the Equine Clinical Practice, Surgery and
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of the project is to investigate how neuroinflammation and the protein annexin A1 modulate vulnerability and resilience in animal models of post-traumatic stress disorder. Applicants are expected to have prior
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21 Feb 2026 Job Information Organisation/Company FAPESP - São Paulo Research Foundation Research Field Environmental science Researcher Profile Established Researcher (R3) Application Deadline 10 Mar 2026 - 23:59 (UTC) Country Brazil Type of Contract To be defined Job Status Not Applicable Is...
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configurations hinders the creation of generalizable solutions for processing these images. This project proposes an innovative approach that combines state-of-the-art diffusion models with physical radar