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Postdoctoral Research Fellow: Computational Methods for Fluid-Granular Flows Job No.: 690226 Location: Clayton campus Employment Type: Full-time Duration: 3-year fixed-term appointment Remuneration
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, specifications and presentations for internal reviews and external conferences or journals Job Requirements: Preferably PhD in computer engineering, Computer Science, Electronics Engineering or equivalent
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The job details are shown below. For further information click on the link for the job description. The University of Plymouth is seeking an experienced Research Assistant to work in an NIHR-funded MHRG Mental Health Research Group. This ambitious initiative aims to strengthen mental health...
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evidence base and strategic recommendations to accelerate housing delivery and the adoption of Modern Methods of Construction (MMC) across a major regeneration area. Working as part of the project team, you
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to accelerate housing delivery and the adoption of Modern Methods of Construction (MMC) across a major regeneration area. Working as part of the project team, you will contribute to research design, data
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. To collaborate with industrial and academic partners. To perform any other duties related to the research program. Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics
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Engineering, or related field. At least 3 years of relevant experience in computer vision, artificial intelligence, etc. Proficiency in programming languages such as C and Python Proficiency in deep learning
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: PhD’s degree in computer engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical, proactive and a team player Excellent teamwork and verbal, written
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Applications are invited for the following full-time position in the Saw Swee Hock School of Public Health: Research Fellow or Research Associate (Digital Health – Mixed Methods Research), 1 year
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methods and deep learning to enable scientific reasoning. Develop software prototypes for automated research workflows that integrate autonomous discovery pipelines with modern deep learning architectures