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leading-edge control theory / mathematical systems theory research at a high international level. The recruited applicant will be expected to publish outstanding research in IEEE Transactions on Automatic
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contributing to new advances in deep learning methodology for cardiac ultrasound imaging. Deep learning methods for cardiac ultrasound can e.g. be used for automatic measurements from image sequences, to assess
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at the university campus in Stavanger. The core element of this PhD project is to use physics-based and data-driven modeling approaches for developing accurate models for real-time optimization and control
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will be identifying the mineralogical and textural controls on the mineralogical and petrophysical signatures of unmineralized and mineralized rocks (with Dr. A Melo, UCD). With Professor Gleeson
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of the applicant. Relevant aspects may include but are not limited to: compositional multiphase reservoir simulation upscaling or screening methodologies optimization of well positions and control strategies
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candidate will lead the development of an activity-level carbon calculation methodology and digital tool that automatically tracks emissions across project lifecycle, integrating data science, ontologies
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structured and handle a heavy workload having a good command of both oral and written English Requirements for competence in English A good proficiency in English is required for anyone attending the PhD
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programme Reference Number AE2025-0543 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2025-0543
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will acquire. Access to career guidance will be provided throughout the doctoral education. Research topic The PhD Fellow will be affiliated with the subject area of customs and border control. Since
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deducted as a contribution to the Norwegian Public Service Pension Fund. automatic membership in the Norwegian Public Service Pension Fund, which provides favourable insurance- and retirement benefits