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Processing and Image Analysis Group, Section for Machine Learning, Department of Informatics. You will be part of Visual Intelligence and the DSB group. For more information about the position see https
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, proteomics, metabolomics), Capacity to develop and/or apply : Statistical or mathematical models Machine learning / AI methods Systems biology modeling approaches Research position The fellow will conduct
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samples. Apply machine learning and deep learning techniques to automate segmentation and quantitative analysis of tomographic refractive-index data from cells and tissue samples. Apply the developed
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. The core research objective of this PhD is to design and evaluate “latency hiding” methods for immersive networked interactions. This involves (i) developing predictive machine learning models that forecast
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The University of British Columbia (UBC) | Vancouver UBC, British Columbia | Canada | about 2 months ago
expertise in artificial intelligence (AI), machine learning (ML), and data science. The position will be a part of the Walk Tall research team based at BC Children’s Hospital. The Postdoctoral Fellow will
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clinical approaches, including: Histopathology and digital pathology (whole-slide imaging, WSI) Quantitative analysis of the tumour immune microenvironment AI-based image analysis, machine learning and deep
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simulations, machine-learned force fields, and artificial intelligence (AI). The successful candidate will lead the development of a computational platform that unifies first-principles methods, classical
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the Digital Signal Processing and Image Analysis Group, Section for Machine Learning, Department of Informatics. You will be part of Visual Intelligence and the DSB group. For more information about the
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recently funded centre of excellence (Integreat). Integreat collects scientists from statistics and computer science and offers a flourishing machine learning community, including many PhDs and PostDocs
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by ARPES, pursue scalable wafer-scale moiré epitaxy, develop epitaxial superconductors for quantum computing and integrate machine learning for automated high-throughput MBE. We are particularly