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Field
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Your Job: Explore bio-inspired algorithms through simulation—both numerical and circuit-based—and experiment with existing hardware, including CMOS and memristor circuits. Additionally, will need
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Approximation. Parameterized Complexity is a vastly growing area within theoretical computer science that allows for the development of exact and approximation algorithms for computationally hard problems by
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microscopy and atom probe tomography will be prepared. Finally, you will merge the images by means of deep learning algorithms. Your tasks in detail Development of the experimental protocol for the imaging
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field” imaging techniques to solve many important problems in biology and change clinical practice in respiratory medicine. Our ongoing research program involves developing new imaging technologies
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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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of advanced computational techniques. This research will integrate power system modelling, optimisation algorithms, and artificial intelligence (AI) techniques to develop an innovative framework for strategic
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for Mathematics (KdVI) and the Informatics Institute (IvI), as well as Deep Blue Capital. This unique setting gives you the opportunity to combine rigorous academic research with real-world applications in
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Technische Universität Berlin, Electrical Engineering and Computer Science (Faculty IV) Position ID: Technische Universität Berlin -Electrical Engineering and Computer Science (Faculty IV) -PHD
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Vacancies PhD position on Dependability Driven on Device Learning Algorithms for Embedded Neuromorphic Architectures Key takeaways Edge devices that can learn autonomously while guaranteeing
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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