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for Artificial Intelligence (FCAI), ELLIS Institute Finland, and Aalto University House of AI, invites applications for multiple postdoctoral positions. Our team works actively to develop intelligent robotic
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researched and developed that will be used in current and future key topics. Become a part of our team and join us on our journey of research and innovation! What you will do Testing new deep learning
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leverage low-precision accelerators for scientific computing by using a number of tricks, known as "mixed-precision" algorithms. Developing such algorithms is far from trivial. We can look at computational
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PhD position on Closed-loop testing for faster and better EM evaluation of complex high-tech systems
that you help us to solve that problem! The objectives are to develop a world-wide accepted new standard for testing large systems in RC, by comparing the newest fast EM sensor systems, comparing performance
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Biology in Cancer Genomics and Immunology. This position will be involved in the development and/or application of computational approaches to understand the mechanism of cancer development, progression
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to conduct interdisciplinary collaborative research in BNL programs with a highly competitive salary. Essential Duties and Responsibilities: Conduct research and develop novel AI/ML algorithms and
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at Griffith University. We are seeking applicants with active research interests in quantum computing, quantum algorithm/software development and applications in decarbonisation who can take full advantage
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position in statistics, machine learning, and data science. The postdoctoral researcher will be mentored by Professor Yiyuan She and will contribute to the development of innovative statistical methods and
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electromagnetic data during drilling. This includes the further development and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous
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formulation, which displays striking similarities to that used by the Computational Fluid Dynamics (CFD) community, has inspired the investigators to adopt conventional CFD algorithms in the novel context