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required. •Experience with deep-learning techniques and libraries welcome. •Past experience in neutrino oscillation and/or LAr TPC experiment welcome. Website for additional job details https
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quantitative discipline, completed before the appointment begins. Strong Python programming skills and hands-on experience in machine learning or deep learning. Demonstrated ability to conduct original research
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. The ideal candidate will possess not only a deep conceptual understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. We
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are passionate about building a positive environment to grow and groom future business leaders. And we are looking to add to our BIZ family! To learn more about the NUS Business School, please visit https
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 hour ago
(e.g., machine/deep learning, big data infrastructure and computing, or other emerging approaches) in an applied domain relevant to the department’s strengths and areas of growth including, but not
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PhD studentship: Discovery of rational therapeutic biomarkers in breast cancer by systems pathology and deep learning Supervisor: Dr Hamid Raza Ali Department/location:Cancer Research UK Cambridge
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methods capable of identifying manufacturing defects and predicting downstream performance outcomes of Diesel Particulate Filter (DPF) units. Combining computer vision, machine learning, deep learning, and
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appointed 0.4fte. Key responsibilities include: Designing and delivering learning that prepares students for the responsibilities of senior clinical placements and Foundation Year (FY) training. Developing
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The Role We’re looking for an experienced engineer who has a broad understanding of VMware, and a deep understanding of at least one major supporting operating system. You will collaborate closely
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interface of machine learning, deep learning, data science and applications in forest sciences. Together with the Director, you will further develop KIForst as a faculty-wide platform for methodological