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Field
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you eager to make AI more sustainable? As a PhD Candidate, you will develop innovative methods for predicting and reducing the energy consumption of large-scale AI systems during their design phase
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a big plus: Relevant publications (and/or M.Sc. thesis) on the above-mentioned research topics Programming Microcontrollers and Interfacing Sensors Machine Learning Algorithms and Deep Neural Networks
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of the Khovanov–Seidel representation in new cases, using Garside-type approaches. Developing extensions of Garside theory to the case of infinitely many atoms may also be of interest and will rely on the study of
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Systems (DESS) research group and supervised by Professor Torben Bach Pedersen. Within Digital Energy, DESS has developed the award-winning FlexOffer technology, one of the few open, general, and scalable
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information into versatile benchmarks supporting development of a new generation of assured PNT systems. Positioning, navigation, and timing (PNT) underpin modern transportation, logistics, and critical
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and access to the latest technologies Exciting opportunities for professional and personal development Safe and stable working environment Company pension funds We offer an annual gross salary
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the individual process parameters on the target properties and develop predictive machine learning model; iii) based on the machine learning algorithms, develop PBF-LB Mg alloy with defined microstructure
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
Thermography. This raw dataset is needed to be processed and annotated to train supervised and unsupervised AI models. The research will aim to develop deep learning algorithms for damage classification
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team. Significant software development experience in several key languages, e.g., Rust, C++, or Python (not MATLAB), algorithms, and machine learning is necessary as well as excellent communication
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Everyone is talking about artificial intelligence. But who is developing the necessary chips? We are, for example! Would you like to help drive the development of a new highly efficient AI hardware