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problems, numerical mathematics, optimisation, machine learning and imaging physics, with applications ranging from medical and industrial imaging to geophysics. For more information, please visit
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; Proven competence on flow measurement techniques and PIV; Familiarity with optics, lasers, image processing and statistical data analysis Familiarity with flow modelling techniques (CFD) or machine
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PhD Scholarship Develop AI and machine learning models to guide real-time, personalised treatment of paediatric brain cancer using multiomics and clinical data, within the internationally
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SINTEF, Equinor, and Total. The main objectives of the project include the development and the integration of signal processing and machine learning methodologies aiming to improve flow assurance via field
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knowledge, innovations, and solutions that help move the world forward. Faculty Mechanical Engineering From chip to ship. From machine to human being. From idea to solution. Driven by a deep-rooted desire to
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Start date – 1st March 2027 (latest) Project Description Machining generates large quantities of swarf, with many components losing 60–90% of their material during the machining stage. This swarf
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various relevant computer software applications. • Assist with literature research, editing and preparing manuscripts, scientific illustrations and computer graphics for publication. • May perform
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engineering disciplines, including structural mechanics, hydrodynamics and machine learning Strong programming skills in Python and/or MATLAB Experience with scientific computing, CFD/FEM software, potential
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Engineering or related disciplines, provided you have a strong interest in communication systems and networking. If you have a solid technical background and are excited about future wireless and satellite
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diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation, evidence-based policymaking, science-policy communication, and policy brief writing