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candidate in the exciting area of multiscale and multiphysics modelling of sustainable fibrous composites, with additional focus on uncertainty quantification and machine learning. Information The context
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The AITHYRA-CeMM Joint International PhD Call in Molecular Medicine and Artificial Intelligence (m/f
-20 fully funded PhD positions here: https://apply.cemm.at/ Supported by the Medical University of Vienna, the Technical University of Vienna and University of Vienna, the AITHYRA and CeMM PhD programs
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, statistics, or mathematics OR a strong background in gene engineering and functional interrogation of hematopoietic stem and progenitor cells. Strong knowledge in bioinformatics, machine learning, statistics
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students. Assist in grant proposal applications. Requirements: PhD in Mechanical Engineering, Machine Learning, Artificial Intelligence, Computational Mechanics, Material Science, Industrial Engineering, or
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-reviewed publications in areas of extragalactic astronomy that are synergistic with the functional work on NED. Experience with Machine Learning or Artificial Intelligence. Proficiency in Python, working
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Science (Ref: FST241110) Job Description Candidates with expertise in one or more of the following areas: Database Management Systems, Programming Languages, Machine Learning, Data Mining, Data Processing
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, tightly collaborating research groups conducting research and education in Robotics and AI, Intelligent Autonomous Systems, Robot Learning, Machine Learning, Human-Robot Interaction and Natural
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of To apply: Please send your CV mentioning reference CF-246 at info@phd-jobs.net Contact person: Vitaly Pryadka Tel.:+40 (765) 734 800 Web: http://www.phd-jobs.net Where to apply E-mail info@phd-jobs.net
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Pryadka Tel.: +40 (765) 734 800 Web: http://www.phd-jobs.net Where to apply E-mail info@phd-jobs.net Requirements Research FieldMedical sciences » MedicineEducation LevelBachelor Degree or equivalent
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WaterWeave project, which focuses on innovative solutions for monitoring and the sustainable management of water resources. The fellow will develop machine learning and cloud computing techniques to estimate