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of individual atoms. The development of this technology is crucial for the next generation of quantum device fabrication techniques and has great commercial interest and potential for patenting. The skills and
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) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness of the assessment. All components assembled
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activities, i.e., teaching and supervision of BSc and MSc student projects at DTU. We are looking for candidates with Strong skills in AI, Machine Learning, and/or Data Science, preferably with experience in
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(e.g., Kalman Filter) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness
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research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet
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qualification you must hold a PhD degree (or equivalent). Specifically, a PhD in manufacturing engineering (or equivalent) with documented experience in the following areas: Precision machining Machining system
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consumers. You'll gain deep interdisciplinary experience—combining multiple data layers and approaches including bioinformatics, machine learning, food safety management, regulatory science, genomics and user
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computationally efficient numerical structural models. To support the condition (state) assessment, the project will also explore the use of advanced estimators (e.g., Kalman Filter) or Machine Learning models
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conducting high-quality research at the intersection of thermo-fluids science, AI/machine learning and optimization. We envision that: You have an open mind and can think creatively in engineering
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, the CAPeX approach to finding new electrocatalytic materials for energy conversion reactions uses state-of-the-art machine learning techniques, but experimental feedback is needed to improve the models and