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matching, optimal transport or cell-cycle modelling. Key Responsibilities These include but are not limited to: Leading an independent research project in scientific machine learning and mechanistic
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research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and fairness, brings rich diversity to UBC as a workplace, and creates the
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focused on using spatial profiling and machine learning of human specimens in combination with functional experiments in animal models to understand cancer initiation, progression, and metastasis. We
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 13 days ago
it, 80 years later. For generations, our people have turned curiosity into ideas that have shaped Australia and our region. Learn more about ANU, our history and the community that has shaped us: ANU
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within five years of the award of their PhD. Preference is given to those who have recently completed their doctorate, have not previously held a postdoctoral position, and/or are on the academic
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modalities in the rodent brain (3D light-sheet microscopy, spatial transcriptomics and MRI of the same brain) to learn MRI contrasts that can depict neuronal and glial cell density and morphology. You will
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collaborative and interdisciplinary activities. Job Requirements: Preferably PhD degree in Computer Engineering, Computer Science, Applied Mathematics or equivalent. Strong academic background in machine learning
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 11 days ago
curiosity into ideas that have shaped Australia and our region. Learn more about ANU, our history and the community that has shaped us: ANU at 80 The Australian National University is seeking a motivated
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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to build ‘design-build-test-learn’ pipeline through computational design and experimental validation. About you PhD (or in the final stages of PhD submission) in computational biology, evolutionary genomics