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Koziarski Lab - The Hospital for Sick Children | Central Toronto Roselawn, Ontario | Canada | about 1 month ago
program at The Hospital for Sick Children, University of Toronto, and the Vector Institute. Our research group focuses on developing machine learning-based pipelines that leverage generative models and
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) and reproducible research practices Desirable criteria Experience working with generative models or large language models Experience with large scale GPU-based model training and cloud computing
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the development of new anchoring technologies for floating offshore wind turbines. Your role will focus primarily on the development of simplified computational models, and you will contribute
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the Sterne-Weiler Lab i n Computational Biology / Discovery Oncology. The postdoctoral position is focused on developing and applying foundational AI models to investigate clinically relevant cancer
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Department of Health Technology and Informatics Postdoctoral Fellow / Part-time Research Associate (Ref. 251014023) (1) Postdoctoral Fellow [Appointment period: twelve months] (2) Part-time Research
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will join a dynamic, interdisciplinary team exploring the frontiers of mass spectrometry imaging and molecular modelling. As our Research Fellow, you will lead the computational arm of a Leverhulme Trust
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development; application or development of computational or statistical methods; data analysis; modeling; statistics and machine learning; scientific simulation; or theoretical astrophysics Effective oral and
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within and beyond the University. We are looking for a Research Fellow to research on the areas of 3D printing technologies using advanced modeling and simulation. Key Responsibilities: Development
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schools and research centres within and beyond the University. We are looking for a Research Fellow to research on the areas of 3D printing technologies using advanced modeling and simulation. Key
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models of program adoption and diffusion. Contribute to the broader research agenda by undertaking additional scholarly inquiry and program evaluation as required to advance innovation, policy relevance