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are looking for candidates who have experience with developing AI or machine learning models, as well as bacterial sequence analysis. You should be familiar with relevant programming languages such as Python
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industrial processes. Your research will drive a paradigm shift in how TES systems are modelled, integrated, and controlled within industrial settings. You will develop novel, adaptive, physics-informed models
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data Design algorithms for correlating low-level events into process-level attack models Contribute to joint framework development with TU/e on continual learning Collaborate with industry partners
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Job Description The project takes place in the Quantum Light Sources group at DTU Electro, where we design, model, fabricate and test sources of single photons or entangled photon pairs
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models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train these methods in a closed-loop
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to circularity and sustainability in the footwear industry. Through advanced experimental work and modeling, you will help uncover how the composition of shoes will affect the circularity potential, and advice
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of the Marie Skłodowska-Curie Doctoral Network SCARPA, a European training network committed to circularity and sustainability in the footwear industry. Through advanced experimental work and modeling
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cement based on literature, theory, and experiments. Optimization of composition with other waste materials. Thermodynamic modelling and experiments with advanced technologies are used. Development of a
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or ethnic background are encouraged to apply. As DTU works with research in critical technology, which is subject to special rules for security and export control, open-source background checks may be
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background are encouraged to apply. As DTU works with research in critical technology, which is subject to special rules for security and export control, open-source background checks may be conducted