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addressing research questions relevant to data science, biology, and agroecology the aim is to improve data flows and create knowledge graphs and future visions of landscapes. The tasks will encompass
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Engineering, Machine Learning, Applied Mathematics, or a related field. A strong academic background and interest in AI systems, embedded intelligence, edge computing, machine learning, or related areas. Strong
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circuit models and algorithms for estimating the charge, health, and power based on direct methods (e.g. open circuit voltage), model-based methods (e.g. Kalman filtering), data driven methods (e.g. machine
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Are you an experienced researcher in microbial genomics and bioinformatics with a strong record of university teaching, and expertise in whole-genome sequencing (WGS) analysis, machine learning and
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Two DTU Tenure Track Assistant Professors in Autonomous Modelling and in Robotic Synthesis of Ene...
and robotics. As the successful candidate, you will develop innovative research programs spanning atomistic and mesoscopic materials simulations, machine learning, foundation and surrogate models
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within this field. Your work tasks In this position you will conduct research within Computer Vision and Deep Learning, with a particular focus on the development of an AI-powered framework
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of Civil and Architectural Engineering Theme 9: Building the resilient, flexible, multi-energy microgrids of tomorrow hosted by the Department of Electrical and Computer Engineering Theme 10: Advanced
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conduct research ranging from basic health research to translational and clinical research with a focus on digital health. Apart from striving for excellence within specialized disciplines, it is our vision
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Join a research environment where Artificial Intelligence and Machine Learning moves beyond theory into clinical impacts. At the Faculty of Engineering and Science, this postdoctoral position offers
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level, from the quantum processor to the quantum-classical interface all the way to quantum algorithms and applications. The vision of the programme is to enable the development of fault-tolerant quantum