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simulation tasks. A key objective will be the development and testing of algorithms for gas hydrate phase equilibrium calculations. CapSim seeks to advance CO₂ capture simulation technology by enhancing
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-disciplinary involving algorithmics, stochastic optimization, multi-criteria decision making, and data science. As part of the project, you will implement and test algorithms and further develop your skills in
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. The candidate will collaborate closely with team members to develop novel quantum computing algorithms that leverage hybrid detection methods and explore the potential of 3D cluster states for enabling fault
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sets and developing algorithms. You should be highly motivated, self-driven, and possess strong work ethics, team spirit, and excellent collaboration skills. You will be responsible for the collection
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behaviour. This will include developing and using state-of-the-art image recognition algorithms to create digital twin models as well as statistical and machine learning methods for analysing large-scale
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hardware modification. The AI will learn and adapt the realms of the combustion modes and fine tune the performance for each while the engine is operated. Self-tuning, adaptive, control algorithms will be
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potential market failures and prevention mechanisms. You will be combining theoretical analysis with practical applications, involving mathematical modeling, algorithm development, and coding. You should have
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meetings Potentially participate in Arctic field campaigns Be working with large data sets and developing algorithms. You should be highly motivated, self-driven, and possess strong work ethics, team spirit
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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling