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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
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novel multi-objective optimisation algorithms, to evaluate metrics such as material circularity, system efficiency, cost, and carbon footprint. The University of Surrey is ranked 12th in the UK in
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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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. Key Accountabilities • Design and develop embedded AI algorithms for appliance profiling using smart meter data • Benchmark performance against state-of-the-art NILM approaches using datasets like
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technology. ▪ Close connection to the activities of the Munich Quantum Valley with its main goal to build a quantum computer based on different platforms, to develop suitable algorithms and applications, and
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This PhD project aims to advance Safe and Sustainable by Design (SSbD) pharmaceutical manufacturing by integrating cutting-edge methodologies, including computer-assisted retrosynthesis, end-to-end
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
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areas, and be able to creatively combine disciplines to make new research advances in fluid mechanics. You will be creating data-driven algorithms which can solve state estimation problems in fluid