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Field
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science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning
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research training leading to the successful completion of a PhD degree. For more information see: http://www.mn.uio.no/english/research/phd/ All candidates and projects will have to undergo a check versus
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information theory, and quantum technologies. For additional information, please visit: https://dakic.univie.ac.at/ . Your future tasks: You will actively participate in research, teaching & administration
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into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract
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LevelMaster Degree or equivalent Skills/Qualifications We are looking for an ambitious PhD candidate with the following requirements: 1. Data Integration and Management Ability to compile and harmonize large
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diffraction data where the information extends towards 3-d space. Machine learning offers promising approaches for the solution of complex problems of disorder, ultimately aiming at general and automated
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The information for the PhD admission is available at TalTech´s web-page: https://taltech.ee/en/phd-admission The following application documents should be sent to francesco.deluca@taltech.ee CV Motivation letter
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outcomes of therapy. The lab is looking for candidates for the following two stipends: • Stipend 1: Computer Vision-Based Analysis of Humans. This PhD candidate will focus on developing new AI/computer
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Machine Learning; (ii) Big Data and Data Management; (iii) Computer Vision and Pattern Recognition; and (iv) Distributed Systems and Networking. These key research areas have a special thematic focus on (a
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and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains from chem- and bioinformatics to computer vision and social network analysis. Machine learning with