172 machine-learning-"https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" PhD positions in Denmark
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employees and 10 research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning
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digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design
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candidate is expected to hold: A master degree in biomedical engineering or computer science, Excellent programming skills (Python). Experience with data curation, large-scale datasets, and machine learning
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. You will also spend 3 months at Georgia Tech/Emory University (USA), working on machine learning and data benchmarking. Work description The selected PhD student will be responsible for the full
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, sensing at the robot–environment interface, and bioinspired control strategies to allow the robot to perceive and adapt to different terrains. By bridging soft robotics, physical intelligence, and learning
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(supervised by Assoc. Prof. Ivana Konvalinka) and machine learning researchers (co-supervised by Prof. Lars Kai Hansen), you will be responsible for designing and running interactive multi-person (hyperscanning
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electrical energy storage systems; energy management systems. Experience with data processing, statistical analysis and machine learning techniques is an advantage. Knowledge with Mathworks suite, C/C++ and
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of these materials. Implementation of artificial intelligence (AI) and machine learning (ML) to establish the connection between the existing models and material data (both literature and the baseline established in
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. This fellowship aims to develop and utilize experimental methods for the collection of experimental results for enabling the use of machine learning, that will allow us to characterize the digestive processes
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mechanisms and kinetics to stabilize highly active but metastable surface motifs sustainable catalytic processes. Modeling Atomic Processes on Nanoparticles Develop atomistic models and machine-learning