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Field
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This PhD project aims to advance Safe and Sustainable by Design (SSbD) pharmaceutical manufacturing by integrating cutting-edge methodologies, including computer-assisted retrosynthesis, end-to-end
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for candidates with a background in meteorology, climatology, physics, engineering and any related discipline, and a strong interest in applying advanced physical and computational methods to real-world
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: The main place of work will be at our campus in Halden, but some presence at our campus in Fredrikstad may be expected. Project description Project title: My AI Co-worker: Exploring AI for Computer Supported
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assess risks of de-anonymization, including researching current de-anonymization strategies Deploying and benchmarking anonymization methods. Investigation of state-of-the-art methods from cryptography and
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PhD Scholarship in Digital Mapping of Homemade & DIY Cultural Economies in First Nations Communities
, and grassroots creativity that may be undervalued in formal cultural economies The project will innovate methodologically (leveraging methods from human-centred design and digital sociology) to generate
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data requirements, and lower costs for large-scale modelling tasks. PINNs enhance predictive capabilities and efficiency by combining data-driven methods with physical principles. Unlike traditional
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microanalysis methods to reveal how tramp elements introduced during recycling impact microstructure and properties in aluminium mega-castings. As a PhD student, you will be supported by a multidisciplinary team
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We are offering a PhD student position in machine learning (ML) theory, focusing on new methods for training models with a limited amount of data. The student will be a part of a new NEST initiative
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/ physics/ meteorology/ engineering/ biology. Experience in a computer programming language (e.g. Fortran, Python). A general understanding of the importance of the scientific method is essential. A previous
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-based method to approximate the CFD-revealed effects of liquid metal convection on molten pool temperature predictions. • Designing and conducting instrumented WA-DED experiments to validate the developed