44 distributed-algorithm-"Meta"-"Meta"-"Meta" positions at Technical University of Denmark in Denmark
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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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techniques for integrating such solutions into modern SDV middleware. Responsibilities: Conduct research in runtime analysis and reconfiguration of in-vehicle TSN networks. Develop algorithms and prototypes
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integrate machine learning algorithms and Earth System Models to emulate carbon processes in the ocean connected to the biological activities. You will be enrolled in DTU’s Section for Oceans and Arctic and
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thus including sensing systems, tool condition features selection, algorithms for automated signal preprocessing, feature extraction and decision making based on ML and AI. An integral part of
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systems. We develop design technologies and computing platforms for distributed and embedded systems, with applications in IoT, Edge AI, safety-critical systems, and quantum computing. We are seeking new
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of the effects of climate change, habitats and recovery, dynamics and distribution of stocks, interactions between species (such as prey and predators), recreational and commercial fisheries, and the integration
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to have experience with: Phase equilibrium calculation algorithms and their integration into CO2 capture simulation Thermodynamic modeling of phase equilibrium and thermophysical properties related to CO2
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solutions and policy impacts. You will design and implement machine-learning algorithms that interact with your simulation framework for scenario discovery, building surrogate models of simulation outputs
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will develop in the position; it is expected that you have previous experience on each of them: Develop and implement CFD models to simulate the behavior of PRO systems. Apply ML algorithms to optimise