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. The project focuses on developing an integrated approach that combines machine learning techniques with physics-based models to estimate the health of various system components. The aim is that fault diagnosis
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. Please turn on JavaScript in your browser and try again. UiO/Anders Lien 24th March 2026 Languages English English English Join the inclusive University of Oslo as a PhD Research Fellow in Machine Learning
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YOUNG RESEARCHER IN THE FIELD OF EARLY DETECTION OF THE HEALTH STATUS OF PLANTS USING REMOTE SENSING
and features for the pre-symptomatic detection of changes in the physiological status of plants, developing, training, validating, and comparing predictive machine learning and deep learning models
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tomorrow. Where to apply Website https://academicpositions.com/ad/eth-zurich/2026/phd-position-computer-simulati… Requirements Research FieldEngineeringYears of Research Experience1 - 4 Research
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. Research Associate in Bioinformatics and Machine Learning Applications to Stem Cell Bioengineering The School of Biomedical Engineering at the University of British Columbia, Vancouver campus is seeking a
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, test and measurement methodologies for electronic modules, system engineering, data pre-processing and database indexing/analytics for dashboarding/visualisation, embedding machine learning algorithms
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programs to eligible team members. Learn more at: https://hr.duke.edu/benefits/ Equal Opportunity Employer: Duke is an Equal Opportunity Employer committed to providing employment opportunity without
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Computer science » Computer systems Computer science » Programming Technology » Communication technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 26 Apr 2026 - 23
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topics such as statistics, high performance programming, machine learning and using data to constrain cosmological models. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs
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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