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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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biased, outdated, or sensitive data? That's where the project TRAI comes in. This research project aims to develop machine unlearning algorithms to selectively erase specific knowledge from trained AI
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properties of skeletal muscle during static and dynamic contractions. The student will also participate in early-stage algorithmic work to model muscle architecture and behavior across contraction types. In
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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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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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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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enable the development of fault-tolerant quantum computing hardware and quantum algorithms that solve life-science-relevant chemical and biological problems. Within NQCP we are currently pursuing several
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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