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of Engineering Science, in collaboration with Professor Rafael Perera-Salazar (Nuffield Department of Primary Care Health Sciences, University of Oxford). You should possess a relevant PhD (or near completion*) in
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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision
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-infected cells. Carry out structural analyses on purified samples using cryo-EM/cryo-ET. Perform infection and cell-based assays using iPSC-derived cell lines. Collaborate with cross-functional teams across
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help us in our pursuits. Learn more about us by visiting our website https://cosc.arch.tamu.edu/ . What we want The Department of Construction Science at Texas A&M University invites applications for a
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collaboration with industry partners and SMEs. You have a track record of scientific publications in relevant venues. You are proficient in Python and relevant machine learning frameworks such as PyTorch
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 1 month ago
data analysis methods to study biological memory circuits and their applications to machine learning. Building on recent work from the Fiete Lab, the role focuses on identifying principles of biological
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mechanisms underlying risk and resilience. You will work with a rich multimodal dataset and collaborate within an international network spanning computational psychiatry, clinical psychology, and neuroscience
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) for pediatric and fetal cardiovascular applications. The successful candidate will work within a collaborative environment that integrates clinical cardiology, biomedical engineering, and computational imaging
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human health. Within this mission, the Iorio Group works at the intersection of computational biology, functional genomics, and precision oncology, integrating machine learning, large-scale CRISPR
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, engineers, and students working on addressing environmental challenges such as industrial emissions, urban pollution, and climate change impacts. ARC_AIR also collaborates closely with national and